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Submodular-based view selection for low-quality points rendering with multi-feature point-based NeRF
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AI
DOI：10.1007/s00371-025-03944-3.png)
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Abstract
En 中文
NeRF has revolutionized view synthesis and 3D reconstruction. However, significant challenges persist when using RGB/RGB-D sensors for 3D reconstruction. Issues such as uneven lighting, object occlusions, or incomplete scanning processes often result in incomplete reconstructed point clouds. These incomplete point clouds may occur even when the captured images contain comprehensive information about the entire scene. In order to effectively tackle these challenges, we propose a robust approach that includes three essential components: (1) a submodular-driven view selection strategy that maximizes scene coverage from limited views. (2) A multi-feature fusion technique that combines point, voxel, and pixel features enhances scenes with low-quality point cloud rendering. Point features are derived from neighboring surface points, voxel features are learned through 3D-UNet, and pixel features are extracted from pre-selected high-quality views. (3) A hybrid rendering approach balancing non-trainable and trainable features for efficient, high-quality rendering. Experiments on NeRFSynthetic and Scannet datasets demonstrate that our approach improves rendering and reconstruction quality, particularly in scenes with low-quality point clouds, outperforming existing point-based neural rendering methods across various environmental conditions.
Keywords:
NeRF
3D reconstruction
Point cloud completion
Neural rendering
Submodular optimization