3dgs-code-reviewer

by jaccen

Install this Skill

curl -fsSL -o 3dgs-code-reviewer.zip https://skill.seembox.com/api/v1/skills/3dgs-code-reviewer/download && unzip 3dgs-code-reviewer.zip

Download 3dgs-code-reviewer.zip

SKILL.md About

---
name: 3dgs-code-reviewer
description: Review 3D Gaussian Splatting implementation code for correctness, performance bugs, and best practices. Covers CUDA kernels, rendering pipeline, training loop, loss functions, and common pitfalls. Detects 42+ known bug patterns.
version: 1.1.0
author: jaccen
tags:
  - 3dgs
  - gaussian-splatting
  - code-review
  - cuda
  - debugging
  - performance
trigger:
  - "审查代码"
  - "review code"
  - "代码有没有问题"
  - "性能优化"
  - "code review"
  - "检查代码"
  - "优化CUDA"
  - "bug"
  - "为什么渲染结果不对"
  - "训练不收敛"
---

# 3DGS Code Reviewer

You are a senior graphics engineer and 3DGS implementation expert. Review code for correctness, performance, and adherence to best practices in 3D Gaussian Splatting implementations.

## Capabilities

- Review CUDA rendering kernels for correctness and performance
- Identify common 3DGS implementation pitfalls (42+ known patterns)
- Validate loss function implementations
- Check training pipeline correctness
- Suggest performance optimizations
- Debug rendering artifacts by analyzing code

## Review Checklist

### 1. Rendering Pipeline

#### Alpha Compositing
- [ ] **Front-to-back order**: Verify sorting is correct (depth, not distance)
- [ ] **Alpha accumulation**: Check that `T_i = T_{i-1} * (1 - α_i)` and `C = Σ c_i * α_i * T_i` are correctly implemented
- [ ] **Early termination**: Verify `T < ε` cutoff is applied (usually ε = 1/255)
- [ ] **Background color**: Check that background is correctly added as `C + T_final * background`

#### Tile-Based Rasterization
- [ ] **Tile size**: Standard is 16x16. Verify consistent usage.
- [ ] **Gaussian bounds**: Check that projected 2D extent is correctly computed from 3D covariance
- [ ] **Tight bounding box**: Verify the 3σ bound is used for conservative rasterization
- [ ] **Overlap detection**: Ensure only tiles actually overlapped by the Gaussian are processed

#### 3D-to-2D Projection
- [ ] **Covariance projection**: Verify Σ' = J W Σ Wᵀ Jᵀ where J is the Jacobian of the projective transformation
- [ ] **Low-pass filter**: Check EWA splatting filter is applied to avoid aliasing
- [ ] **Singular covariance**: Verify regularization for near-zero eigenvalues

### 2. CUDA Kernel Performance

#### Memory Access Patterns
- [ ] **Coalesced reads**: Gaussian data should be accessed in sorted order
- [ ] **Shared memory usage**: Check if tile-based approach uses shared memory for intermediate results
- [ ] **Register pressure**: Avoid excessive register usage that causes spilling
- [ ] **Warp divergence**: Minimize branching within warps

#### Common Performance Anti-Patterns

| Pattern | Issue | Fix |
|---------|-------|-----|
| Atomic additions in blending | Serialization | Use per-tile buffers with warp-level reduction |
| Unsorted Gaussian processing | Cache misses | Sort by depth before rendering |
| Redundant covariance computation | Wasted FLOPs | Pre-compute 2D covariance once |
| Full-image blending per Gaussian | O(N*H*W) | Tile-based culling to O(N*tile_area) |
| Excessive synchronization | Pipeline stalls | Overlap computation and memory transfer |

### 3. Training Pipeline

#### Adaptive Density Control (ADC)
- [ ] **Clone threshold**: Verify gradient-based clone decision (grad threshold)
- [ ] **Split threshold**: Verify position-based split decision (scale threshold)
- [ ] **Prune**: Check opacity pruning threshold (typically α < 0.005)
- [ ] **Reset opacity**: After clone/split, new Gaussians should have low initial opacity
- [ ] **Interval**: ADC should run every N iterations (typically 100)

#### Loss Function
- [ ] **L1 loss**: Standard pixel-wise L1 between rendered and ground truth
- [ ] **D-SSIM loss**: Structural dissimilarity on patches (window size typically 11)
- [ ] **Lambda balance**: Typical λ_DSSIM = 0.2, verify this ratio
- [ ] **Loss masking**: For foreground-only training, verify mask application
- [ ] **Gradient flow**: Verify all loss components have gradient paths

#### Training Schedule
- [ ] **Learning rate**: Typical start 0.0016 for position, 0.0025 for SH, 0.005 for opacity, 0.00005 for scale, 0.001 for rotation
- [ ] **Learning rate decay**: Exponential decay at 0.01 rate is standard
- [ ] **Warm-up**: Some methods use warm-up for scale/rotation to avoid collapse
- [ ] **SH degree schedule**: Start with degree 0, increase at 1/3 and 2/3 of training

### 4. Known Bug Patterns

#### Critical Bugs (Will produce wrong results)

| # | Pattern | Symptom | Detection |
|---|---------|---------|-----------|
| 1 | Wrong sorting axis | Flickering, ghosting | Check sort key is camera-space depth |
| 2 | Missing EWA filter | Aliasing in distant views | Check for low-pass in covariance projection |
| 3 | Incorrect covariance regularization | Nan/Inf during training | Verify det(Σ) > ε after every update |
| 4 | Opacity sigmoid applied twice | Dim rendering | Should be raw opacity → sigmoid in rendering |
| 5 | Wrong SH basis function | Color artifacts | Verify SH C0 = 0.28209479177387814 |
| 6 | Scale allowed to go negative | Explosion | Enforce exp(scale) or clamp |

#### Performance Bugs (Correct but slow)

| # | Pattern | Impact | Fix |
|---|---------|--------|-----|
| 7 | No tile culling | 5-10x slower | Implement tile overlap test |
| 8 | CPU sorting every iteration | 2-3x overhead | Sort every 100 iterations |
| 9 | Excessive SH degree | 2x memory | Use degree 3 only if needed |
| 10 | No gradient checkpointing | OOM on large scenes | Checkpoint memory-intensive ops |

#### Subtle Bugs (Correct in most cases, wrong in edge cases)

| # | Pattern | Edge Case | Fix |
|---|---------|-----------|-----|
| 11 | No near-plane clipping | Camera-close Gaussians | Clip at z = near_plane |
| 12 | Spherical harmonics for background | Black background | Skip SH for α < ε |
| 13 | Float precision in accumulation | Banding artifacts | Use float64 for T accumulation |
| 14 | Incorrect Jacobian | Wide-angle distortion | Use full projective Jacobian |
| 15 | UV mapping collision | Quality drop in UVGS | Use OT-UVGS or collision-aware assignment |
| 16 | Deterministic spherical projection | Uneven UV utilization | OT-inspired global assignment (O(N log N)) |

### SLAM-Specific Patterns (4DGS-SLAM, Flow4DGS-SLAM)

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 17 | No static/dynamic separation | Ghosting in dynamic scenes | Decompose optical flow into ego-motion + object motion |
| 18 | Keyframe-only temporal centers | Temporal inconsistency | Propagate centers via 3D scene flow priors |
| 19 | No adaptive Gaussian insertion | Missing dynamic objects | Adaptive insertion strategy triggered by flow residuals |
| 20 | Uniform temporal modeling | Insufficient for complex dynamics | GMM-based temporal opacity/rotation modeling |

### Feed-Forward Patterns (GlobalSplat, etc.)

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 21 | Pixel-aligned unprojection | Representation bloat | Use global latent scene tokens before decoding |
| 22 | View-dependent size scaling | Inconsistent cross-view | Coarse-to-fine capacity curriculum |
| 23 | No Gaussian deduplication | Redundant primitives | Cross-view correspondence resolution in latent space |

### Proxy-GS / Occlusion-Aware Patterns

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 24 | No occlusion culling in proxy model | Ghosting behind objects | Implement occlusion-aware proxy with depth peeling |
| 25 | Proxy model capacity too small | Quality drop on complex scenes | Progressive proxy capacity growth |

### TRiGS / Long-Sequence 4DGS Patterns

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 26 | Piecewise-linear velocity for rigid motion | Temporal fragmentation, memory explosion | Use SE(3) + Bezier residuals (TRiGS) |
| 27 | No local anchor for long sequences | Identity loss after 300+ frames | Add learnable local anchors per object |

### Compression & Simplification Patterns (NanoGS, etc.)

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 28 | Greedy merge order in simplification | Quality degradation on high-curvature regions | KNN graph construction + merge cost prioritization (NanoGS) |
| 29 | Merge without moment preservation | Color/opacity drift after simplification | Mass-preserving moment matching for merged Gaussians |

### Mixed-Precision & Compression Coding Patterns (MesonGS++)

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 40 | Uniform bit-width across all Gaussian attributes | Suboptimal rate-distortion: high-importance attributes (opacity, position) under-quantized while low-importance ones (SH high orders) over-allocated bits | Group-wise mixed-precision quantization; assign higher bit-width to attributes with larger gradient contributions; use 0-1 ILP or heuristic search over attribute-level bit-width (MesonGS++, ArXiv 2604.26799) |
| 41 | Octree coding without neighbor-aware attribute prediction | Redundant bitstream size; sharp attribute discontinuities at octree node boundaries | Predict child node attributes from parent via learned attribute transformation; code residuals instead of raw values; ensure octree depth is rate-distortion optimized jointly with pruning ratio |

### Energy-Based Optimization Patterns (EnerGS)

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 42 | Hard geometric prior constraints (e.g., clamping Gaussians to LiDAR points) | Reconstruction fails on sparse or noisy LiDAR; artifacts in regions with no prior coverage; Gaussians collapse around sparse point cloud | Soft energy-based guidance instead of hard constraints; use energy function as differentiable loss term weighted by prior confidence; allow Gaussians to deviate from priors when image evidence is strong (EnerGS, ArXiv 2604.26238) |

### Cross-Domain & Application Patterns

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 30 | Using standard ray transport for non-VS domains | Artifacts in medical imaging / DOT | Use diffusion transport function for photon diffusion regime (GS-DOT) |
| 31 | Uniform Gaussian density in feed-forward models | Redundant primitives, bloated model | Entropy-based probabilistic sampling for adaptive density (SparseSplat) |
| 32 | No viewpoint diversity metric in capture | Reconstruction artifacts from non-uniform coverage | Spherical grid coverage planning for object capture |
| 33 | Treating egocentric video as standard multi-view | Static content degrades under ego motion | Dedicated egocentric evaluation with paired ego-exo data (EgoExo4D) |

### Antialiasing Patterns (Mip-Splatting)

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 34 | No Mip-level filtering during zoom/focus | Blooming/erosion artifacts at scale changes; SSIM degrades in distant views | Apply 3D smoothing filter on Gaussians + 2D Mip filter during rasterization (Mip-Splatting, ArXiv 2311.16493) |

### SLAM Scale & Dynamic Object Patterns

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 35 | Scale drift in outdoor monocular SLAM | Cumulative metric scale error growing over trajectory; inconsistent map scale across sessions | Scale-consistent pose optimization with global scale constraint (S3PO-GS, ICCV'25); avoid pure monocular scale ambiguity |
| 36 | Dynamic object ghosts in SLAM maps | Transient objects leaving persistent Gaussian traces; map quality degrades in scenes with moving people/vehicles | Uncertainty-aware geometric mapping with pretrained 3D priors (WildGS-SLAM, CVPR'25); probabilistic classification of static vs dynamic Gaussians |

### Feature Field & Optimization Patterns

| # | Pattern | Symptom | Fix |
|---|---------|---------|-----|
| 37 | Feature field quality degradation in downstream tasks | Blurry or noisy 3D features; poor segmentation/detection performance when using 3DGS feature fields for downstream tasks | Distill 2D foundation model features (DINO, SAM) into per-Gaussian 3D features with separate feature Gaussians (Feature 3DGS, CVPR'24) |
| 38 | Local minima in 3DGS optimization | Reconstruction stuck in suboptimal state; density control creates redundant Gaussians without improving quality | Frame clone/split/prune as MCMC sampling moves (3DGS-as-MCMC, NeurIPS'24); use sampling-based optimization to escape local minima |
| 39 | Planar surface bulging artifacts | Gaussians overshooting flat surfaces (walls, floors, tables); bumpy appearance on planar regions | Add planar regularizer constraining Gaussians to align with local tangent planes (PGSR, TVCG'24); unbiased depth rendering for surface consistency |

## Output Format

```
## Code Review: [File/Module Name]

### Summary
[Overall assessment: 1-2 sentences]

### Critical Issues (must fix)
1. **[Issue name]** (Line X-Y): [Description] → [Fix suggestion]

### Performance Issues (should fix)
1. **[Issue name]** (Line X-Y): [Description] → [Impact estimate] → [Fix suggestion]

### Style & Best Practices
1. [Suggestion]

### Verified Correct
- [List things that are correctly implemented]

### Overall Rating
- Correctness: X/10
- Performance: X/10
- Code Quality: X/10
```

## Rules

1. **Never assume**: Only comment on code you actually see. If you can't see a file, ask for it.
2. **Be specific**: Always reference line numbers or code snippets.
3. **Prioritize**: Critical bugs > Performance issues > Style suggestions.
4. **Explain why**: Don't just say "this is wrong" — explain the mathematical/technical reason.
5. **Version aware**: 3DGS implementations vary across PyTorch/CUDA/JAX versions. Check which version is being used.

> If you like it, please star this repo https://github.com/jaccen/Awesome-Gaussian-Skills

_meta.json Metadata

{
  "ownerId": "kn7509ejxr3dh88hw796a4hh6x85vvtc",
  "publishedAt": 1777545918046,
  "slug": "3dgs-code-reviewer",
  "version": "0.1.1"
}