RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo

Author:

Xi Junhua1,Shi Yifei,Wang Yijie1,Guo Yulan1,Xu Kai1

Affiliation:

1. National University of Defense Technology

Funder

NSFC

National Key Research and Development Program of China

Publisher

IEEE

Reference52 articles.

1. Disn: Deep implicit surface network for high-quality single-view 3d reconstruction;xu;ArXiv Preprint,0

2. Learning inverse depth re-gression for multi-view stereo with correlation cost volume;xu;Proceedings of the AAAI Conference on Artificial Intelli-gence,2020

3. Patchnets: Patch-based generalizable deep implicit 3d shape represen-tations;tretschk;European Conference on Computer Vision,0

4. Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes

5. Metasdf: Meta-learning signed distance functions;sitzmann;ArXiv Preprint,2020

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1. CT-MVSNet: Efficient Multi-view Stereo with Cross-Scale Transformer;MultiMedia Modeling;2024

2. A survey on conventional and learning‐based methods for multi‐view stereo;The Photogrammetric Record;2023-08-13

3. Self-Supervised Depth Estimation Based on the Consistency of Synthetic-real Image Prediction;2023 International Conference on Advanced Robotics and Mechatronics (ICARM);2023-07-08

4. Bi-ClueMVSNet: Learning Bidirectional Occlusion Clues for Multi-View Stereo;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

5. Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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