Agent Conversation: deepseek-reasoner
Model: deepseek-reasoner
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Messages: 19
UserStep 0
Task: cv-3dgs-densification
3D Gaussian Splatting Densification Strategy Design
Objective
Design a densification strategy for 3D Gaussian Splatting (3DGS) that achieves the best novel view synthesis quality on real-world scenes.
Background
3D Gaussian Splatting represents scenes as collections of 3D Gaussians optimized via differentiable rendering. A critical component is the densification strategy, which controls how Gaussians are added, split, and pruned during optimization:
- Clone: Duplicate small Gaussians in under-reconstructed regions
- Split: Divide large Gaussians into smaller ones for finer detail
- Prune: Remove transparent or oversized Gaussians
- Reset: Periodically reset opacities to encourage pruning of unneeded Gaussians
Recent work has proposed various improvements:
- AbsGS: Uses absolute gradients instead of average for better detail recovery
- Mini-Splatting: Blur-aware forced splitting + importance-based pruning
- MCMC 3DGS: Treats densification as Markov Chain Monte Carlo sampling
- New Split (Cao et al.): Mathematically consistent Gaussian splitting
Task
Implement a CustomStrategy class in custom_strategy.py. Your strategy controls
the full lifecycle of Gaussians during training via two hooks:
Editable Region
@dataclass
class CustomStrategy(Strategy):
def initialize_state(self, scene_scale: float = 1.0) -> Dict[str, Any]:
# Initialize running statistics for your strategy
...
def step_pre_backward(self, params, optimizers, state, step, info):
# Called BEFORE loss.backward(). Use to retain gradients.
...
def step_post_backward(self, params, optimizers, state, step, info, packed=False):
# Called AFTER loss.backward() and optimizer.step().
# This is where you implement densification logic.
...Available Operations (from gsplat.strategy.ops)
duplicate(params, optimizers, state, mask)— Clone selected Gaussianssplit(params, optimizers, state, mask)— Split selected Gaussians (sample 2 new positions from covariance)remove(params, optimizers, state, mask)— Remove selected Gaussiansreset_opa(params, optimizers, state, value)— Reset all opacities to a valuerelocate(params, optimizers, state, mask, binoms, min_opacity)— Teleport dead Gaussians to live onessample_add(params, optimizers, state, n, binoms, min_opacity)— Add new Gaussians sampled from opacity distributioninject_noise_to_position(params, optimizers, state, scaler)— Perturb positions
Available Information
The info dict from rasterization contains:
means2d: 2D projected means (with.gradafter backward)width,height: Image dimensionsn_cameras: Number of cameras in batchradii: Screen-space radii per Gaussiangaussian_ids: Which Gaussians are visible
The params dict contains Gaussian parameters:
means: [N, 3] positionsscales: [N, 3] log
AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "gsplat/custom_strategy.py",
4 "start_line": 20,
5 "end_line": 90,
6 "content": "# ============================================================================\n# Densification Strategy (EDITABLE REGION: lines 20-90)\n# ============================================================================\n\n@dataclass\nclass CustomStrategy(Strategy):\n \"\"\"Adaptive Percentile Gradient with View Consistency Weighting.\n \n Key innovations:\n 1. Adaptive gradient thresholds using percentiles (top_k_split, top_k_clone)\n 2. View consistency weighting: gradients weighted by visibility count\n 3. Soft opacity reset: gradual decay of low-opacity Gaussians\n 4. Scale-adaptive splitting: large Gaussians with moderate gradient still split\n 5. Combined abs gradient + max gradient blending\n \"\"\"\n \n # Densification thresholds (adaptive via percentiles)\n prune_opa: float = 0.005\n grow_grad2d_percentile_split: float = 0.95 # Top 5% gradients for splitting\n grow_grad2d_percentile_clone: float = 0.90 # Top 10% gradients for cloning\n grow_scale3d: float = 0.01\n prune_scale3d: float = 0.1\n refine_start_iter: int = 500\n refine_stop_iter: int = 18_000 # Later stop for continued refinement\n reset_every: int = 3000\n refine_every: int = 100\n \n # View consistency weighting\n min_views_for_reliable: int = 3 # Minimum views to consider gradient reliable\n \n # Soft reset parameters\n soft_reset_decay: float = 0.9 # Decay factor for low-opacity Gaussians\n soft_reset_boost: float = 1.1 # Boost factor for promising Gaussians\n \n # Gradient blending (like Taming)\n avg_weight: float = 0.6\n max_weight: float = 0.4\n\n def initialize_state(self, scene_scale: float = 1.0) -> Dict[str, Any]:\n \"\"\"Initialize and return the running state for this strategy.\"\"\"\n return {\n \"grad2d\": None, # Accumulated absolute gradients\n \"count\": None, # Visibility count (for averaging)\n \"grad2d_max\": None, # Max gradient per Gaussian (Taming-style)\n \"view_count\": None, # How many cameras see each Gaussian\n \"scene_scale\": scene_scale,\n }\n\n def check_sanity(self, params, optimizers):\n \"\"\"Sanity check for required parameters.\"\"\"\n super().check_sanity(params, optimizers)\n for key in [\"means\", \"scales\", \"quats\", \"opacities\"]:\n assert key in params, f\"{key} is required in params but missing.\"\n\n def step_pre_backward(self, params, optimizers, state, step, info):\n \"\"\"Called BEFORE loss.backward(). Retain gradients for densification.\"\"\"\n info[\"means2d\"].retain_grad()\n\n def step_post_backward(self, params, optimizers, state, step, info,\n packed=False):\n \"\"\"Called AFTER loss.backward(). Implement densification logic here.\"\"\"\n if step >= self.refine_stop_iter:\n return\n\n # Use ABSOLUTE gradients (AbsGS style)\n if hasattr(info[\"means2d\"], \"absgrad\"):\n grads = info[\"means2d\"].absgrad.clone()\n else:\n grads = info[\"means2d\"].grad.abs().clone()\n \n # Scale gradients by image dimensions\n grads[..., 0] *= info[\"width\"] / 2.0 * info[\"n_cameras\"]\n grads[..., 1] *= info[\"height\"] / 2.0 * info[\"n_cameras\"]\n\n n = len(list(params.values())[0])\n if state[\"grad2d\"] is None:\n # Initialize statistics\n state[\"grad2d\"] = torch.zeros(n, device=grads.device)\n state[\"count\"] = torch.zeros(n, device=grads.device)\n state[\"grad2d_max\"] = torch.zeros(n, device=grads.device)\n state[\"view_count\"] = torch.zeros(n, device=grads.device, dtype=torch.long)\n\n # Determine which Gaussians are visible in this batch\n sel = (info[\"radii\"] > 0.0).all(dim=-1) # [C, N] bool\n gs_ids = torch.where(sel)[1] # Gaussian indices\n \n # Compute gradient norms for visible Gaussians\n grad_norms = grads[sel].norm(dim=-1)\n \n # Accumulate gradients with view count weighting\n state[\"grad2d\"].index_add_(0, gs_ids, grad_norms)\n state[\"count\"].index_add_(0, gs_ids, torch.ones_like(gs_ids, dtype=torch.float32))\n \n # Track max gradient per Gaussian (Taming-style)\n state[\"grad2d_max\"].scatter_reduce_(0, gs_ids, grad_norms, reduce=\"amax\", include_self=True)\n \n # Track view count (how many cameras see each Gaussian)\n state[\"view_count\"].index_add_(0, gs_ids, torch.ones_like(gs_ids, dtype=torch.long))\n\n if step > self.refine_start_iter and step % self.refine_every == 0:\n # Compute average gradients\n avg_grads = state[\"grad2d\"] / state[\"count\"].clamp_min(1)\n \n # Blend average and max gradients\n combined = self.avg_weight * avg_grads + self.max_weight * state[\"grad2d_max\"]\n \n # Apply view consistency weighting: more views = more reliable gradient\n view_weight = torch.clamp(state[\"view_count\"].float() / self.min_views_for_reliable, 0.0, 1.0)\n weighted_grads = combined * view_weight\n \n scene_scale = state[\"scene_scale\"]\n \n # Adaptive thresholding via percentiles\n valid_mask = state[\"count\"] > 0\n if valid_mask.sum() > 0:\n valid_grads = weighted_grads[valid_mask]\n split_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_split)\n clone_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_clone)\n else:\n split_threshold = clone_threshold = 0.0\n \n scale_max = torch.exp(params[\"scales\"]).max(dim=-1).values\n is_small = scale_max <= self.grow_scale3d * scene_scale\n \n # Clone small Gaussians with high gradient (above clone threshold)\n is_grad_high_clone = weighted_grads > clone_threshold\n is_dupli = is_grad_high_clone & is_small\n if is_dupli.sum() > 0:\n duplicate(params=params, optimizers=optimizers, state=state, mask=is_dupli)\n \n # Split large Gaussians with high gradient (above split threshold)\n is_grad_high_split = weighted_grads > split_threshold\n is_split = is_grad_high_split & ~is_small\n \n # Also split large Gaussians with moderate gradient but low view count\n # (uncertain areas need more exploration)\n view_uncertain = state[\"view_count\"] < self.min_views_for_reliable\n moderate_grad = weighted_grads > (clone_threshold * 0.5)\n is_split_uncertain = view_uncertain & moderate_grad & ~is_small\n is_split = is_split | is_split_uncertain\n \n # Adjust for newly duplicated Gaussians\n is_split = torch.cat([is_split, torch.zeros(is_dupli.sum(), dtype=torch.bool, device=is_split.device)])\n if is_split.sum() > 0:\n split(params=params, optimizers=optimizers, state=state, mask=is_split, revised_opacity=True)\n \n # Prune low-opacity Gaussians\n opacities = torch.sigmoid(params[\"opacities\"].flatten())\n is_prune = opacities < self.prune_opa\n \n # Also prune oversized Gaussians\n if step > self.reset_every:\n is_prune = is_prune | (scale_max > self.prune_scale3d * scene_scale)\n \n if is_prune.sum() > 0:\n remove(params=params, optimizers=optimizers, state=state, mask=is_prune)\n \n # Soft reset: gradually adjust opacities instead of hard reset\n if step % self.reset_every == 0 and step > 0:\n # Boost promising Gaussians (high gradient, good visibility)\n promising = (weighted_grads > clone_threshold * 0.7) & (state[\"view_count\"] >= 2)\n if promising.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n new_opacities[promising] += torch.log(torch.tensor(self.soft_reset_boost, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Decay low-opacity Gaussians\n low_opacity = opacities < self.prune_opa * 2.0\n if low_opacity.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n new_opacities[low_opacity] -= torch.log(torch.tensor(self.soft_reset_decay, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Reset statistics\n state[\"grad2d\"].zero_()\n state[\"count\"].zero_()\n state[\"grad2d_max\"].zero_()\n state[\"view_count\"].zero_()\n torch.cuda.empty_cache()"
7}Tool ResultStep 1
ERROR: Invalid line range 20..90 (file has 82 lines)
[Current file: gsplat/custom_strategy.py | editable: 20–90 | total: 82 lines]
17|
18|
19| # ============================================================================
20 # Densification Strategy (EDITABLE REGION: lines 20-90)
21 # ============================================================================
22
23 @dataclass
24 class CustomStrategy(Strategy):
25 """Custom 3DGS densification strategy.
26
27 TODO: Design your densification strategy to maximize novel view quality.
28
29 Available operations (from gsplat.strategy.ops):
30 duplicate(params, optimizers, state, mask)
31 Clone Gaussians selected by boolean mask.
32 split(params, optimizers, state, mask, revised_opacity=False)
33 Split Gaussians into 2 new ones sampled from covariance.
34 remove(params, optimizers, state, mas
AssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "filename": "gsplat/custom_strategy.py",
4 "start_line": 20,
5 "end_line": 82,
6 "content": "# ============================================================================\n# Densification Strategy (EDITABLE REGION: lines 20-90)\n# ============================================================================\n\n@dataclass\nclass CustomStrategy(Strategy):\n \"\"\"Adaptive Percentile Gradient with View Consistency Weighting.\n \n Key innovations:\n 1. Adaptive gradient thresholds using percentiles (top_k_split, top_k_clone)\n 2. View consistency weighting: gradients weighted by visibility count\n 3. Soft opacity reset: gradual decay of low-opacity Gaussians\n 4. Scale-adaptive splitting: large Gaussians with moderate gradient still split\n 5. Combined abs gradient + max gradient blending\n \"\"\"\n \n # Densification thresholds (adaptive via percentiles)\n prune_opa: float = 0.005\n grow_grad2d_percentile_split: float = 0.95 # Top 5% gradients for splitting\n grow_grad2d_percentile_clone: float = 0.90 # Top 10% gradients for cloning\n grow_scale3d: float = 0.01\n prune_scale3d: float = 0.1\n refine_start_iter: int = 500\n refine_stop_iter: int = 18_000 # Later stop for continued refinement\n reset_every: int = 3000\n refine_every: int = 100\n \n # View consistency weighting\n min_views_for_reliable: int = 3 # Minimum views to consider gradient reliable\n \n # Soft reset parameters\n soft_reset_decay: float = 0.9 # Decay factor for low-opacity Gaussians\n soft_reset_boost: float = 1.1 # Boost factor for promising Gaussians\n \n # Gradient blending (like Taming)\n avg_weight: float = 0.6\n max_weight: float = 0.4\n\n def initialize_state(self, scene_scale: float = 1.0) -> Dict[str, Any]:\n \"\"\"Initialize and return the running state for this strategy.\"\"\"\n return {\n \"grad2d\": None, # Accumulated absolute gradients\n \"count\": None, # Visibility count (for averaging)\n \"grad2d_max\": None, # Max gradient per Gaussian (Taming-style)\n \"view_count\": None, # How many cameras see each Gaussian\n \"scene_scale\": scene_scale,\n }\n\n def check_sanity(self, params, optimizers):\n \"\"\"Sanity check for required parameters.\"\"\"\n super().check_sanity(params, optimizers)\n for key in [\"means\", \"scales\", \"quats\", \"opacities\"]:\n assert key in params, f\"{key} is required in params but missing.\"\n\n def step_pre_backward(self, params, optimizers, state, step, info):\n \"\"\"Called BEFORE loss.backward(). Retain gradients for densification.\"\"\"\n info[\"means2d\"].retain_grad()\n\n def step_post_backward(self, params, optimizers, state, step, info,\n packed=False):\n \"\"\"Called AFTER loss.backward(). Implement densification logic here.\"\"\"\n if step >= self.refine_stop_iter:\n return\n\n # Use ABSOLUTE gradients (AbsGS style)\n if hasattr(info[\"means2d\"], \"absgrad\"):\n grads = info[\"means2d\"].absgrad.clone()\n else:\n grads = info[\"means2d\"].grad.abs().clone()\n \n # Scale gradients by image dimensions\n grads[..., 0] *= info[\"width\"] / 2.0 * info[\"n_cameras\"]\n grads[..., 1] *= info[\"height\"] / 2.0 * info[\"n_cameras\"]\n\n n = len(list(params.values())[0])\n if state[\"grad2d\"] is None:\n # Initialize statistics\n state[\"grad2d\"] = torch.zeros(n, device=grads.device)\n state[\"count\"] = torch.zeros(n, device=grads.device)\n state[\"grad2d_max\"] = torch.zeros(n, device=grads.device)\n state[\"view_count\"] = torch.zeros(n, device=grads.device, dtype=torch.long)\n\n # Determine which Gaussians are visible in this batch\n sel = (info[\"radii\"] > 0.0).all(dim=-1) # [C, N] bool\n gs_ids = torch.where(sel)[1] # Gaussian indices\n \n # Compute gradient norms for visible Gaussians\n grad_norms = grads[sel].norm(dim=-1)\n \n # Accumulate gradients with view count weighting\n state[\"grad2d\"].index_add_(0, gs_ids, grad_norms)\n state[\"count\"].index_add_(0, gs_ids, torch.ones_like(gs_ids, dtype=torch.float32))\n \n # Track max gradient per Gaussian (Taming-style)\n state[\"grad2d_max\"].scatter_reduce_(0, gs_ids, grad_norms, reduce=\"amax\", include_self=True)\n \n # Track view count (how many cameras see each Gaussian)\n state[\"view_count\"].index_add_(0, gs_ids, torch.ones_like(gs_ids, dtype=torch.long))\n\n if step > self.refine_start_iter and step % self.refine_every == 0:\n # Compute average gradients\n avg_grads = state[\"grad2d\"] / state[\"count\"].clamp_min(1)\n \n # Blend average and max gradients\n combined = self.avg_weight * avg_grads + self.max_weight * state[\"grad2d_max\"]\n \n # Apply view consistency weighting: more views = more reliable gradient\n view_weight = torch.clamp(state[\"view_count\"].float() / self.min_views_for_reliable, 0.0, 1.0)\n weighted_grads = combined * view_weight\n \n scene_scale = state[\"scene_scale\"]\n \n # Adaptive thresholding via percentiles\n valid_mask = state[\"count\"] > 0\n if valid_mask.sum() > 0:\n valid_grads = weighted_grads[valid_mask]\n split_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_split)\n clone_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_clone)\n else:\n split_threshold = clone_threshold = 0.0\n \n scale_max = torch.exp(params[\"scales\"]).max(dim=-1).values\n is_small = scale_max <= self.grow_scale3d * scene_scale\n \n # Clone small Gaussians with high gradient (above clone threshold)\n is_grad_high_clone = weighted_grads > clone_threshold\n is_dupli = is_grad_high_clone & is_small\n if is_dupli.sum() > 0:\n duplicate(params=params, optimizers=optimizers, state=state, mask=is_dupli)\n \n # Split large Gaussians with high gradient (above split threshold)\n is_grad_high_split = weighted_grads > split_threshold\n is_split = is_grad_high_split & ~is_small\n \n # Also split large Gaussians with moderate gradient but low view count\n # (uncertain areas need more exploration)\n view_uncertain = state[\"view_count\"] < self.min_views_for_reliable\n moderate_grad = weighted_grads > (clone_threshold * 0.5)\n is_split_uncertain = view_uncertain & moderate_grad & ~is_small\n is_split = is_split | is_split_uncertain\n \n # Adjust for newly duplicated Gaussians\n is_split = torch.cat([is_split, torch.zeros(is_dupli.sum(), dtype=torch.bool, device=is_split.device)])\n if is_split.sum() > 0:\n split(params=params, optimizers=optimizers, state=state, mask=is_split, revised_opacity=True)\n \n # Prune low-opacity Gaussians\n opacities = torch.sigmoid(params[\"opacities\"].flatten())\n is_prune = opacities < self.prune_opa\n \n # Also prune oversized Gaussians\n if step > self.reset_every:\n is_prune = is_prune | (scale_max > self.prune_scale3d * scene_scale)\n \n if is_prune.sum() > 0:\n remove(params=params, optimizers=optimizers, state=state, mask=is_prune)\n \n # Soft reset: gradually adjust opacities instead of hard reset\n if step % self.reset_every == 0 and step > 0:\n # Boost promising Gaussians (high gradient, good visibility)\n promising = (weighted_grads > clone_threshold * 0.7) & (state[\"view_count\"] >= 2)\n if promising.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n new_opacities[promising] += torch.log(torch.tensor(self.soft_reset_boost, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Decay low-opacity Gaussians\n low_opacity = opacities < self.prune_opa * 2.0\n if low_opacity.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n new_opacities[low_opacity] -= torch.log(torch.tensor(self.soft_reset_decay, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Reset statistics\n state[\"grad2d\"].zero_()\n state[\"count\"].zero_()\n state[\"grad2d_max\"].zero_()\n state[\"view_count\"].zero_()\n torch.cuda.empty_cache()"
7}Tool ResultStep 2
OK: Replaced lines 20..82 with 179 line(s) in gsplat/custom_strategy.py. Editable range: 20–206.
[Current file: gsplat/custom_strategy.py | editable: 20–206 | total: 198 lines]
17|
18|
19| # ============================================================================
20 # ============================================================================
21 # Densification Strategy (EDITABLE REGION: lines 20-90)
22 # ============================================================================
23
24 @dataclass
25 class CustomStrategy(Strategy):
26 """Adaptive Percentile Gradient with View Consistency Weighting.
27
28 Key innovations:
29 1. Adaptive gradient thresholds using percentiles (top_k_split, top_k_clone)
30 2. View consistency weighting: gradients weighted by visibility count
31 3. Soft opacity reset: gradual decay of low-opacity Gaussians
32 4. Scale-adaptive splitting: large
AssistantStep 3test
Tool ResultStep 3
[Test #1] (2 tests remaining; call submit(n=1) to submit this result) ### garden (scripts/train_garden.sh) Training results: Loaded 161 train, 24 val images. Scene scale: 1.23 Initialized 138766 Gaussians Traceback (most recent call last): File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/train_gsplat.py", line 370, in <module> File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/train_gsplat.py", line 327, in train File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/custom_strategy.py", line 158, in step_post_backward RuntimeError: The size of tensor a (144125) must match the size of tensor b (138766) at non-singleton dimension 0 ### bicycle (scripts/train_bicycle.sh) Training results: Loaded 169 train, 25 val images. Scene scale: 1.30 Initialized 54275 Gaussians Traceback (most recen
AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "gsplat/custom_strategy.py",
4 "start_line": 119,
5 "end_line": 198,
6 "content": " if step > self.refine_start_iter and step % self.refine_every == 0:\n # Compute average gradients\n avg_grads = state[\"grad2d\"] / state[\"count\"].clamp_min(1)\n \n # Blend average and max gradients\n combined = self.avg_weight * avg_grads + self.max_weight * state[\"grad2d_max\"]\n \n # Apply view consistency weighting: more views = more reliable gradient\n view_weight = torch.clamp(state[\"view_count\"].float() / self.min_views_for_reliable, 0.0, 1.0)\n weighted_grads = combined * view_weight\n \n scene_scale = state[\"scene_scale\"]\n \n # Adaptive thresholding via percentiles\n valid_mask = state[\"count\"] > 0\n if valid_mask.sum() > 0:\n valid_grads = weighted_grads[valid_mask]\n split_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_split)\n clone_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_clone)\n else:\n split_threshold = clone_threshold = 0.0\n \n scale_max = torch.exp(params[\"scales\"]).max(dim=-1).values\n is_small = scale_max <= self.grow_scale3d * scene_scale\n \n # Determine which Gaussians to duplicate (small & high gradient)\n is_grad_high_clone = weighted_grads > clone_threshold\n is_dupli = is_grad_high_clone & is_small\n \n # Determine which Gaussians to split (large & high gradient)\n is_grad_high_split = weighted_grads > split_threshold\n is_split = is_grad_high_split & ~is_small\n \n # Also split large Gaussians with moderate gradient but low view count\n view_uncertain = state[\"view_count\"] < self.min_views_for_reliable\n moderate_grad = weighted_grads > (clone_threshold * 0.5)\n is_split_uncertain = view_uncertain & moderate_grad & ~is_small\n is_split = is_split | is_split_uncertain\n \n # Perform duplication first\n if is_dupli.sum() > 0:\n duplicate(params=params, optimizers=optimizers, state=state, mask=is_dupli)\n \n # After duplication, the total number of Gaussians increased by is_dupli.sum()\n # We need to adjust the split mask to include zeros for the newly added Gaussians\n # (they shouldn't be split immediately)\n is_split = torch.cat([is_split, torch.zeros(is_dupli.sum(), dtype=torch.bool, device=is_split.device)])\n \n # Perform splitting\n if is_split.sum() > 0:\n split(params=params, optimizers=optimizers, state=state, mask=is_split, revised_opacity=True)\n \n # Prune low-opacity Gaussians\n opacities = torch.sigmoid(params[\"opacities\"].flatten())\n is_prune = opacities < self.prune_opa\n \n # Also prune oversized Gaussians\n if step > self.reset_every:\n is_prune = is_prune | (scale_max > self.prune_scale3d * scene_scale)\n \n if is_prune.sum() > 0:\n remove(params=params, optimizers=optimizers, state=state, mask=is_prune)\n \n # Soft reset: gradually adjust opacities instead of hard reset\n if step % self.reset_every == 0 and step > 0:\n # Boost promising Gaussians (high gradient, good visibility)\n promising = (weighted_grads > clone_threshold * 0.7) & (state[\"view_count\"] >= 2)\n if promising.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n new_opacities[promising] += torch.log(torch.tensor(self.soft_reset_boost, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Decay low-opacity Gaussians\n low_opacity = opacities < self.prune_opa * 2.0\n if low_opacity.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n new_opacities[low_opacity] -= torch.log(torch.tensor(self.soft_reset_decay, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Reset statistics\n state[\"grad2d\"].zero_()\n state[\"count\"].zero_()\n state[\"grad2d_max\"].zero_()\n state[\"view_count\"].zero_()\n torch.cuda.empty_cache()"
7}Tool ResultStep 4
OK: Replaced lines 119..198 with 85 line(s) in gsplat/custom_strategy.py. Editable range: 20–211.
[Current file: gsplat/custom_strategy.py | editable: 20–211 | total: 203 lines]
17|
18|
19| # ============================================================================
20 # ============================================================================
21 # Densification Strategy (EDITABLE REGION: lines 20-90)
22 # ============================================================================
23
24 @dataclass
25 class CustomStrategy(Strategy):
26 """Adaptive Percentile Gradient with View Consistency Weighting.
27
28 Key innovations:
29 1. Adaptive gradient thresholds using percentiles (top_k_split, top_k_clone)
30 2. View consistency weighting: gradients weighted by visibility count
31 3. Soft opacity reset: gradual decay of low-opacity Gaussians
32 4. Scale-adaptive splitting: large
AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "gsplat/custom_strategy.py",
4 "start_line": 119,
5 "end_line": 203,
6 "content": " if step > self.refine_start_iter and step % self.refine_every == 0:\n # Compute average gradients\n avg_grads = state[\"grad2d\"] / state[\"count\"].clamp_min(1)\n \n # Blend average and max gradients\n combined = self.avg_weight * avg_grads + self.max_weight * state[\"grad2d_max\"]\n \n # Apply view consistency weighting: more views = more reliable gradient\n view_weight = torch.clamp(state[\"view_count\"].float() / self.min_views_for_reliable, 0.0, 1.0)\n weighted_grads = combined * view_weight\n \n scene_scale = state[\"scene_scale\"]\n \n # Adaptive thresholding via percentiles\n valid_mask = state[\"count\"] > 0\n if valid_mask.sum() > 0:\n valid_grads = weighted_grads[valid_mask]\n split_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_split)\n clone_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_clone)\n else:\n split_threshold = clone_threshold = 0.0\n \n # Pre-modification statistics (original Gaussians only)\n scale_max_orig = torch.exp(params[\"scales\"]).max(dim=-1).values\n is_small = scale_max_orig <= self.grow_scale3d * scene_scale\n \n # Determine which Gaussians to duplicate (small & high gradient)\n is_grad_high_clone = weighted_grads > clone_threshold\n is_dupli = is_grad_high_clone & is_small\n \n # Determine which Gaussians to split (large & high gradient)\n is_grad_high_split = weighted_grads > split_threshold\n is_split = is_grad_high_split & ~is_small\n \n # Also split large Gaussians with moderate gradient but low view count\n view_uncertain = state[\"view_count\"] < self.min_views_for_reliable\n moderate_grad = weighted_grads > (clone_threshold * 0.5)\n is_split_uncertain = view_uncertain & moderate_grad & ~is_small\n is_split = is_split | is_split_uncertain\n \n # Perform duplication first\n if is_dupli.sum() > 0:\n duplicate(params=params, optimizers=optimizers, state=state, mask=is_dupli)\n \n # After duplication, the total number of Gaussians increased by is_dupli.sum()\n # We need to adjust the split mask to include zeros for the newly added Gaussians\n # (they shouldn't be split immediately)\n is_split = torch.cat([is_split, torch.zeros(is_dupli.sum(), dtype=torch.bool, device=is_split.device)])\n \n # Perform splitting\n if is_split.sum() > 0:\n split(params=params, optimizers=optimizers, state=state, mask=is_split, revised_opacity=True)\n \n # After modifications, recompute scale and opacity for pruning\n scale_max = torch.exp(params[\"scales\"]).max(dim=-1).values\n opacities = torch.sigmoid(params[\"opacities\"].flatten())\n \n # Prune low-opacity Gaussians\n is_prune = opacities < self.prune_opa\n \n # Also prune oversized Gaussians\n if step > self.reset_every:\n is_prune = is_prune | (scale_max > self.prune_scale3d * scene_scale)\n \n if is_prune.sum() > 0:\n remove(params=params, optimizers=optimizers, state=state, mask=is_prune)\n \n # Soft reset: gradually adjust opacities instead of hard reset\n if step % self.reset_every == 0 and step > 0:\n # Boost promising Gaussians (high gradient, good visibility)\n promising = (weighted_grads > clone_threshold * 0.7) & (state[\"view_count\"] >= 2)\n if promising.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n # Only adjust original Gaussians (since weighted_grads only for original)\n # This is okay because new Gaussians inherit parent opacity\n new_opacities[promising] += torch.log(torch.tensor(self.soft_reset_boost, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Decay low-opacity Gaussians\n low_opacity = opacities < self.prune_opa * 2.0\n if low_opacity.sum() > 0:\n new_opacities = params[\"opacities\"].clone()\n new_opacities[low_opacity] -= torch.log(torch.tensor(self.soft_reset_decay, device=new_opacities.device))\n params[\"opacities\"].copy_(new_opacities)\n \n # Reset statistics\n state[\"grad2d\"].zero_()\n state[\"count\"].zero_()\n state[\"grad2d_max\"].zero_()\n state[\"view_count\"].zero_()\n torch.cuda.empty_cache()"
7}Tool ResultStep 5
OK: Replaced lines 119..203 with 91 line(s) in gsplat/custom_strategy.py. Editable range: 20–217.
[Current file: gsplat/custom_strategy.py | editable: 20–217 | total: 209 lines]
17|
18|
19| # ============================================================================
20 # ============================================================================
21 # Densification Strategy (EDITABLE REGION: lines 20-90)
22 # ============================================================================
23
24 @dataclass
25 class CustomStrategy(Strategy):
26 """Adaptive Percentile Gradient with View Consistency Weighting.
27
28 Key innovations:
29 1. Adaptive gradient thresholds using percentiles (top_k_split, top_k_clone)
30 2. View consistency weighting: gradients weighted by visibility count
31 3. Soft opacity reset: gradual decay of low-opacity Gaussians
32 4. Scale-adaptive splitting: large
AssistantStep 6test
Tool ResultStep 6
[Test #2] (1 test remaining; call submit(n=2) to submit this result) ### garden (scripts/train_garden.sh) Training results: Loaded 161 train, 24 val images. Scene scale: 1.23 Initialized 138766 Gaussians Traceback (most recent call last): File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/train_gsplat.py", line 370, in <module> File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/train_gsplat.py", line 327, in train File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/custom_strategy.py", line 189, in step_post_backward RuntimeError: The size of tensor a (929806) must match the size of tensor b (1010833) at non-singleton dimension 0 ### bicycle (scripts/train_bicycle.sh) Training results: Loaded 169 train, 25 val images. Scene scale: 1.30 Initialized 54275 Gaussians Traceback (most recen
AssistantStep 7edit
Tool Input
1{
2 "op": "replace",
3 "filename": "gsplat/custom_strategy.py",
4 "start_line": 186,
5 "end_line": 202,
6 "content": " # Periodic opacity reset (like default baseline)\n if step % self.reset_every == 0 and step > 0:\n reset_opa(params=params, optimizers=optimizers, state=state,\n value=self.prune_opa * 2.0)"
7}Tool ResultStep 7
OK: Replaced lines 186..202 with 4 line(s) in gsplat/custom_strategy.py. Editable range: 20–204.
[Current file: gsplat/custom_strategy.py | editable: 20–204 | total: 196 lines]
17|
18|
19| # ============================================================================
20 # ============================================================================
21 # Densification Strategy (EDITABLE REGION: lines 20-90)
22 # ============================================================================
23
24 @dataclass
25 class CustomStrategy(Strategy):
26 """Adaptive Percentile Gradient with View Consistency Weighting.
27
28 Key innovations:
29 1. Adaptive gradient thresholds using percentiles (top_k_split, top_k_clone)
30 2. View consistency weighting: gradients weighted by visibility count
31 3. Soft opacity reset: gradual decay of low-opacity Gaussians
32 4. Scale-adaptive splitting: large
AssistantStep 8edit
Tool Input
1{
2 "op": "replace",
3 "filename": "gsplat/custom_strategy.py",
4 "start_line": 78,
5 "end_line": 196,
6 "content": " def step_post_backward(self, params, optimizers, state, step, info,\n packed=False):\n \"\"\"Called AFTER loss.backward(). Implement densification logic here.\"\"\"\n if step >= self.refine_stop_iter:\n return\n\n # Use ABSOLUTE gradients (AbsGS style)\n if hasattr(info[\"means2d\"], \"absgrad\"):\n grads = info[\"means2d\"].absgrad.clone()\n else:\n grads = info[\"means2d\"].grad.abs().clone()\n \n # Scale gradients by image dimensions\n grads[..., 0] *= info[\"width\"] / 2.0 * info[\"n_cameras\"]\n grads[..., 1] *= info[\"height\"] / 2.0 * info[\"n_cameras\"]\n\n n = len(list(params.values())[0])\n if state[\"grad2d\"] is None:\n # Initialize statistics\n state[\"grad2d\"] = torch.zeros(n, device=grads.device)\n state[\"count\"] = torch.zeros(n, device=grads.device)\n state[\"grad2d_max\"] = torch.zeros(n, device=grads.device)\n state[\"view_count\"] = torch.zeros(n, device=grads.device, dtype=torch.long)\n\n # Determine which Gaussians are visible in this batch\n sel = (info[\"radii\"] > 0.0).all(dim=-1) # [C, N] bool\n gs_ids = torch.where(sel)[1] # Gaussian indices\n \n # Compute gradient norms for visible Gaussians\n grad_norms = grads[sel].norm(dim=-1)\n \n # Accumulate gradients with view count weighting\n state[\"grad2d\"].index_add_(0, gs_ids, grad_norms)\n state[\"count\"].index_add_(0, gs_ids, torch.ones_like(gs_ids, dtype=torch.float32))\n \n # Track max gradient per Gaussian (Taming-style)\n state[\"grad2d_max\"].scatter_reduce_(0, gs_ids, grad_norms, reduce=\"amax\", include_self=True)\n \n # Track view count (how many cameras see each Gaussian)\n state[\"view_count\"].index_add_(0, gs_ids, torch.ones_like(gs_ids, dtype=torch.long))\n\n # Densification every refine_every steps after refine_start_iter\n if step > self.refine_start_iter and step % self.refine_every == 0:\n # Compute average gradients\n avg_grads = state[\"grad2d\"] / state[\"count\"].clamp_min(1)\n \n # Blend average and max gradients\n combined = self.avg_weight * avg_grads + self.max_weight * state[\"grad2d_max\"]\n \n # Apply view consistency weighting: more views = more reliable gradient\n view_weight = torch.clamp(state[\"view_count\"].float() / self.min_views_for_reliable, 0.0, 1.0)\n weighted_grads = combined * view_weight\n \n scene_scale = state[\"scene_scale\"]\n \n # Adaptive thresholding via percentiles\n valid_mask = state[\"count\"] > 0\n if valid_mask.sum() > 0:\n valid_grads = weighted_grads[valid_mask]\n split_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_split)\n clone_threshold = torch.quantile(valid_grads, self.grow_grad2d_percentile_clone)\n else:\n split_threshold = clone_threshold = 0.0\n \n # Pre-modification statistics (original Gaussians only)\n scale_max_orig = torch.exp(params[\"scales\"]).max(dim=-1).values\n is_small = scale_max_orig <= self.grow_scale3d * scene_scale\n \n # Determine which Gaussians to duplicate (small & high gradient)\n is_grad_high_clone = weighted_grads > clone_threshold\n is_dupli = is_grad_high_clone & is_small\n \n # Determine which Gaussians to split (large & high gradient)\n is_grad_high_split = weighted_grads > split_threshold\n is_split = is_grad_high_split & ~is_small\n \n # Also split large Gaussians with moderate gradient but low view count\n view_uncertain = state[\"view_count\"] < self.min_views_for_reliable\n moderate_grad = weighted_grads > (clone_threshold * 0.5)\n is_split_uncertain = view_uncertain & moderate_grad & ~is_small\n is_split = is_split | is_split_uncertain\n \n # Perform duplication first\n if is_dupli.sum() > 0:\n duplicate(params=params, optimizers=optimizers, state=state, mask=is_dupli)\n \n # After duplication, the total number of Gaussians increased by is_dupli.sum()\n # We need to adjust the split mask to include zeros for the newly added Gaussians\n # (they shouldn't be split immediately)\n is_split = torch.cat([is_split, torch.zeros(is_dupli.sum(), dtype=torch.bool, device=is_split.device)])\n \n # Perform splitting\n if is_split.sum() > 0:\n split(params=params, optimizers=optimizers, state=state, mask=is_split, revised_opacity=True)\n \n # After modifications, recompute scale and opacity for pruning\n scale_max = torch.exp(params[\"scales\"]).max(dim=-1).values\n opacities = torch.sigmoid(params[\"opacities\"].flatten())\n \n # Prune low-opacity Gaussians\n is_prune = opacities < self.prune_opa\n \n # Also prune oversized Gaussians\n if step > self.reset_every:\n is_prune = is_prune | (scale_max > self.prune_scale3d * scene_scale)\n \n if is_prune.sum() > 0:\n remove(params=params, optimizers=optimizers, state=state, mask=is_prune)\n \n # Reset statistics after densification\n state[\"grad2d\"].zero_()\n state[\"count\"].zero_()\n state[\"grad2d_max\"].zero_()\n state[\"view_count\"].zero_()\n torch.cuda.empty_cache()\n\n # Periodic opacity reset (independent of densification)\n if step % self.reset_every == 0 and step > 0:\n reset_opa(params=params, optimizers=optimizers, state=state,\n value=self.prune_opa * 2.0)"
7}Tool ResultStep 8
OK: Replaced lines 78..196 with 120 line(s) in gsplat/custom_strategy.py. Editable range: 20–205.
[Current file: gsplat/custom_strategy.py | editable: 20–205 | total: 197 lines]
17|
18|
19| # ============================================================================
20 # ============================================================================
21 # Densification Strategy (EDITABLE REGION: lines 20-90)
22 # ============================================================================
23
24 @dataclass
25 class CustomStrategy(Strategy):
26 """Adaptive Percentile Gradient with View Consistency Weighting.
27
28 Key innovations:
29 1. Adaptive gradient thresholds using percentiles (top_k_split, top_k_clone)
30 2. View consistency weighting: gradients weighted by visibility count
31 3. Soft opacity reset: gradual decay of low-opacity Gaussians
32 4. Scale-adaptive splitting: large
AssistantStep 9test
Tool ResultStep 9
[Test #3] (0 tests remaining) ### garden (scripts/train_garden.sh) Training results: Loaded 161 train, 24 val images. Scene scale: 1.23 Initialized 138766 Gaussians TRAIN_METRICS: step=5000, loss=0.0277, l1=0.0181, ssim_loss=0.0662, num_gs=5516198 Traceback (most recent call last): File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/train_gsplat.py", line 370, in <module> File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/train_gsplat.py", line 327, in train File "/home/yangyucheng/MLS-Bench-main/vendor/workspace/cv-3dgs-densification/deepseek-reasoner_20260419_090603/gsplat/custom_strategy.py", line 137, in step_post_backward RuntimeError: quantile() input tensor is too large ### bicycle (scripts/train_bicycle.sh) Training results: Loaded 169 train, 25 val images. Scene scale: 1.30 Initialized 54275 Gaussians TRAIN_METRICS: step=5000, loss=0.0949, l1