VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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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 perscene 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. Code is available at https://github.com/IVRL/VolRecon/. Show more
Publication status
publishedExternal links
Book title
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Pages / Article No.
Publisher
IEEEEvent
Subject
3D from multi-view and sensorsOrganisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
Related publications and datasets
Is supplemented by: https://github.com/IVRL/VolRecon/
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ETH Bibliography
yes
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