VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction

• URL: http://arxiv.org/abs/2212.08067v2
• Date: Mon, 3 Apr 2023 06:54:50 GMT
• Title: VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
• Authors: Yufan Ren, Fangjinhua Wang, Tong Zhang, Marc Pollefeys and Sabine S\"usstrunk
• Abstract summary: VolRecon is a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF) On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction.
• Score: 64.09702079593372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize per-scene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct the scene with fine details and little noise, VolRecon combines projection features aggregated from multi-view features, and volume features interpolated from a coarse global feature volume. Using a ray transformer, we compute SRDF values of sampled points on a ray and then render color and depth. On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction. Furthermore, our approach exhibits good generalization performance on the large-scale ETH3D benchmark.

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