المفاهيم الأساسية
PointNeRF++ introduces a multi-scale representation for point-based neural rendering, outperforming existing methods on challenging scenes with sparse or incomplete point clouds.
الملخص
The content introduces PointNeRF++, a novel approach that addresses the limitations of existing neural rendering methods when dealing with sparse or incomplete point clouds. The method aggregates point clouds at multiple scale levels, incorporating a global voxel to enhance rendering quality. By unifying classical and point-based NeRF formulations, PointNeRF++ achieves superior performance on various datasets compared to state-of-the-art methods.
Introduction:
Neural Radiance Fields (NeRF) have revolutionized novel-view synthesis but face challenges in uncontrolled scenarios.
Leveraging point clouds can enhance scene representations and renderings.
Method:
Introduces a multi-scale representation for point cloud-based rendering.
Aggregates points at various scale levels using voxel grids.
Utilizes a tri-plane representation for coarser scales to cover larger support regions effectively.
Results:
Outperforms existing methods on datasets like NeRF Synthetic, ScanNet, and KITTI-360.
Provides sharper renderings in challenging real-world scenarios with sparse or incomplete point clouds.
الإحصائيات
Neural Radiance Fields have increased dramatically - NeRF [29]
Peak Signal-to-Noise Ratio (PSNR) - 20.05
Structural Similarity Index (SSIM) - 0.665
اقتباسات
"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."