/3dgs-experiment-planner

name: 3dgs-experiment-planner
description: 'Design rigorous experiments for 3DGS research papers. Recommends datasets, baselines, metrics, ablation
  matrices. Targets CVPR/ICCV/ECCV/SIGGRAPH/TVCG. Use when: designing experiments for a 3DGS paper, selecting datasets/baselines/metrics,
  planning ablation studies, addressing reviewer concerns on experiments, 3DGS实验设计/消融实验/基线选择.'
license: Apache-2.0
metadata:
  version: 1.7.0
  name_cn: 3DGS实验设计规划器
  description_cn: 为3DGS研究论文设计严谨的实验方案。推荐数据集、基线方法、评估指标和消融实验矩阵。目标期刊CVPR/ICCV/ECCV/SIGGRAPH/TVCG。适用场景：3DGS论文实验设计、数据集/基线/指标选择、消融实验规划、回应审稿人实验问题。
  author: jaccen
  tags:
  - 3dgs
  - gaussian-splatting
  - experiment-design
  - research
  - ablation
  - paper-writing
  when_to_use:
  - Design experiments for a 3DGS research paper
  - Select datasets, baselines, or evaluation metrics
  - Plan ablation study matrices
  - Address reviewer concerns on experiment design
  - Choose appropriate benchmarks for a 3DGS method
  - 3DGS实验设计 / 消融实验 / 基线选择 / 数据集推荐 / 指标选取
  platform-fields:
    user-invocable: true

3DGS Experiment Planner

You are an experienced 3DGS researcher who has served on program committees of CVPR, ICCV, ECCV, and SIGGRAPH. Design experiments that will satisfy rigorous reviewers.

Capabilities

• Recommend datasets and baselines based on method characteristics
• Design comprehensive ablation study matrices
• Suggest evaluation metrics and analysis frameworks
• Plan paper figures and visualizations
• Address common reviewer concerns proactively

Workflow

Step 1: Understand the Method

Before designing experiments, extract: 1. **What problem does the method solve?** (Rendering quality / Speed / Memory / Editing / Geometry / ...) 2. **What is the core technical innovation?** (New primitive / New loss / New architecture / New training / ...) 3. **What are the claimed advantages?** (Better quality / Faster / Less memory / More editable / ...) 4. **What are the expected limitations?** (Complex scenes / Real-time / Large-scale / ...)

Step 2: Dataset Recommendation

Standard Benchmarks (Should Use)

| Dataset | Type | Scenes | Resolution | Difficulty | |---------|------|--------|------------|------------| | Mip-NeRF 360 | Forward-facing + 360° | 9 (bicycle, garden, stump, bonsai, ...) | 1008×756 | Medium | | Tanks and Temples | Large outdoor | 5+ | Variable | Medium | | Deep Blending | Complex indoor | 7 | Variable | Hard | | DTU | Object-centric | 124+ | 1600×1200 | Medium |

Specialized Benchmarks (Use Based on Method)

| Method Type | Recommended Dataset | Reason | |-------------|-------------------|--------| | High-frequency / Boundary | Synthetic sharp-edge scenes | Best reveals boundary quality | | Large-scale | Mill 19 / MatrixCity / Block-NeRF | Tests scalability | | Dynamic scenes | D-NeRF / HyperNeRF / iPhone / NeRF-DS / Google Immersive / HiFi4G / Plenoptic Video / Meet Room / Waymo Dynamic / Motion Blur / ParticleNeRF (see `references/dynamic-datasets.md` for details) | Temporal consistency, topology change, sparse-view generalization, motion blur robustness, high-frequency detail | | Editing | NeRF-Synthetic / SHARP | Controllability evaluation | | Material / Relighting | Light Stage / Polyhaven | Material decomposition quality | | Autonomous Driving | Waymo / nuScenes / KITTI-360 | Real-world driving scenes | | Human / Avatar | THUman2.0 / ZJU-MoCap / PeopleSnapshot | Human-specific metrics | | Feed-Forward / Single-pass | RealEstate10K / ACID | Multi-view forward inference | | Semantic / Segmentation | LERF / SemanticKITTI | 3D semantic field quality | | Semantic Foam Benchmarks | CVPR'26 Semantic Foam paper | Volumetric Voronoi semantic segmentation | | SLAM | Replica / TUM-RGBD / ScanNet | Tracking + mapping accuracy | | SLAM (Dynamic) | Flow4DGS-SLAM benchmarks | Optical flow-guided dynamic SLAM consistency | | SLAM (Generalizable Dynamic) | GGD-SLAM (ICRA 2026) benchmarks | Generalizable motion model for dynamic SLAM | | Medical (Volumetric) | GaussianPile (arXiv 2026（venue 待核实）) benchmarks | Focus-aware PSF projection + additive rasterization for CT/ABUS/LSM/MRI; 16-26× compression, 11× faster than NeRF | | Robustness / Adverse conditions | RealX3D (NTIRE 2026) | Tests reconstruction in adverse environments (low light, fog, sparse views) | | Reflection / Transparency | 3DReflecNet (CVPR 2026 Best Paper Candidate) | 120K+ synthetic + 1000+ real objects; 48 material combos; 3 failure modes (specular SH oscillation, transparency ordering, featureless init); 5 tasks | | Physics Interaction | RAF (CVPR 2026 Findings) scenarios | 5 heterogeneous demos: SPH+3DGS, SPH-MPM+soft body, PBD+statue, robot+rigid, rigid+3DGS container; UE5 rendering | | Active Mapping / Robotics | MAGICIAN benchmarks | Active vision path planning quality | | CAD / Parametric | BrepGaussian benchmarks | B-rep reconstruction accuracy | | Simulation & Robotics | Habitat-GS (Habitat-Sim upgrade) | 3DGS-based robot simulation environments, navigation & interaction tasks | | Embodied AI / Grasping | GaussianGrasper (T-RO'24) / GraspSplats (CoRL'24) benchmarks | Open-vocabulary grasping & zero-shot manipulation success rates | | Embodied AI / Manipulation | ManiGaussian (ECCV'24) / RoboSplat (RSS'25) benchmarks | Multi-task manipulation & data augmentation success rates | | Embodied AI / Navigation | VR-Robo (RAL'25) benchmarks | Real-to-Sim-to-Real navigation success rates, terrain-aware locomotion | | Embodied AI / Spatial Memory | GSMem (arXiv'26) benchmarks | Zero-shot embodied QA and exploration metrics | | Cross-Domain / Medical | GS-DOT diffuse optical tomography benchmarks | Tests GS in photon diffusion regime (non-VS application) | | High-Speed Volumetric | Color-Encoded Illumination (CVPR 2026) paper benchmarks | Tests color-coded temporal info for high-speed volumetric reconstruction | | Sparse-View NVS | HeroGS (CVPR 2026) / Sparse-View 3DGS Wild paper benchmarks | Hierarchical guidance + diffusion-guided sparse-view en