Research map / Point clouds / Point cloud classification
Boosting Point Clouds Rendering via Radiance Mapping
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use radiance mapping with coordinate rectification instead of NPBG++ | point-cloud rendering · NeRF-Synthetic | PSNR: 30.52 -> 31.74 | Helped |
| use radiance mapping with coordinate rectification instead of NPBG++ | point-cloud rendering · ScanNet | PSNR: 25.24 -> 25.88 | Helped |
| use radiance mapping with coordinate rectification instead of NPBG++ | point-cloud rendering · DTU | PSNR: 30.25 -> 30.81 | Helped |
| use radiance mapping with coordinate rectification instead of NeRF | rendering on point-cloud scenes · NeRF-Synthetic | PSNR: 33.07 -> 31.74 | Hurt |
| use radiance mapping with coordinate rectification instead of NeRF | rendering on point-cloud scenes · ScanNet | PSNR: 25.63 -> 25.88 | Helped |
| use radiance mapping with coordinate rectification instead of NeRF | rendering on point-cloud scenes · DTU | PSNR: 31.03 -> 30.81 | Hurt |
| add coordinate rectification (ablation) | Radiance Mapping · NeRF-Synthetic Chair, Drums, Lego and Mic at 1× density | PSNR: 32.56 -> 33.63; 24.57 -> 25.05; 26.48 -> 27.16; 32.36 -> 33.44 | Helped |
| increase refinement feature dimensions by a factor of 4 (ablation) | Radiance Mapping · NeRF-Synthetic Chair, Drums, Lego and Mic at 1× density | PSNR: 33.63 -> 34.13; 25.05 -> 25.53; 27.16 -> 27.46; 33.44 -> 34.44 | Helped |
| increase refinement feature dimensions by a factor of 4 (ablation) | Radiance Mapping · NeRF-Synthetic | model size: 127M | Hurt |
Closest papers by meaning
- Sparse Point Cloud Patches Rendering via Splitting 2D Gaussians (CVPR 2025 · similarity 0.87)
- Depth-Guided Robust and Fast Point Cloud Fusion NeRF for Sparse Input Views (AAAI 2024 · similarity 0.86)
- Rad-NeRF: Ray-decoupled Training of Neural Radiance Field (NeurIPS 2024 · similarity 0.86)
- NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields (AAAI 2024 · similarity 0.86)
- GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting (ICLR 2025 · similarity 0.86)
- HashPoint: Accelerated Point Searching and Sampling for Neural Rendering (CVPR 2024 · similarity 0.85)
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