Centrala begrepp
Multi-scale representation enhances point-based NeRF for improved rendering quality.
Sammanfattning
The content introduces PointNeRF++, a multi-scale, point-based neural radiance field method that addresses issues with sparse or incomplete point clouds. By aggregating points at multiple scale levels and incorporating a global voxel, the method outperforms existing NeRF-based methods on various datasets. The approach unifies classical and point-based NeRF formulations, providing better novel-view synthesis in challenging real-world scenarios.
Introduction:
- Neural Radiance Fields (NeRF) have revolutionized novel-view synthesis.
- Challenges persist in applying NeRF to real-world scenarios with sparse camera overlaps.
- Leveraging point clouds can enhance scene representations.
Method:
- Multi-scale aggregation strategy is introduced to handle sparsity in point clouds.
- Tri-plane representation replaces MLPs for coarser scales.
- Global voxel inclusion unifies classical and point-based NeRF formulations.
Results:
- Outperforming state-of-the-art methods on datasets like KITTI-360, ScanNet, and NeRF Synthetic.
- Significantly improving renderings in regions with low point cloud density or no points.
Ablation Study:
- Number of scale levels impacts performance, with the global scale playing a crucial role.
- Tri-plane representation performs slightly better than MLPs at coarsest scales.
- Adding a global voxel improves overall performance compared to using local scales only.
- The method remains effective even at drastic downsampling rates of the point cloud.
Conclusion:
The multi-scale approach of PointNeRF++ shows promise in addressing challenges with sparse or incomplete point clouds, pushing the boundaries of novel-view synthesis closer to practical applications.
Statistik
To deal with sparsity, we average across multiple scale levels—but only among those that are valid, i.e., that have enough neighboring points in proximity to the ray of a pixel.
Our method achieves the best performance among methods that supervise only with color on KITTI-360 dataset.
Citat
"Our solution leads to much better novel-view synthesis in challenging real-world situations with sparse or incomplete point clouds."
"We introduce an effective multi-scale representation for point-based NeRF."