CVRecon: Rethinking 3D Geometric Feature Learning For Neural
Reconstruction
- URL: http://arxiv.org/abs/2304.14633v3
- Date: Thu, 14 Sep 2023 22:15:15 GMT
- Title: CVRecon: Rethinking 3D Geometric Feature Learning For Neural
Reconstruction
- Authors: Ziyue Feng, Liang Yang, Pengsheng Guo, Bing Li
- Abstract summary: We propose an end-to-end 3D neural reconstruction framework CVRecon.
We exploit the rich geometric embedding in the cost volumes to facilitate 3D geometric feature learning.
- Score: 12.53249207602695
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in neural reconstruction using posed image sequences have
made remarkable progress. However, due to the lack of depth information,
existing volumetric-based techniques simply duplicate 2D image features of the
object surface along the entire camera ray. We contend this duplication
introduces noise in empty and occluded spaces, posing challenges for producing
high-quality 3D geometry. Drawing inspiration from traditional multi-view
stereo methods, we propose an end-to-end 3D neural reconstruction framework
CVRecon, designed to exploit the rich geometric embedding in the cost volumes
to facilitate 3D geometric feature learning. Furthermore, we present
Ray-contextual Compensated Cost Volume (RCCV), a novel 3D geometric feature
representation that encodes view-dependent information with improved integrity
and robustness. Through comprehensive experiments, we demonstrate that our
approach significantly improves the reconstruction quality in various metrics
and recovers clear fine details of the 3D geometries. Our extensive ablation
studies provide insights into the development of effective 3D geometric feature
learning schemes. Project page: https://cvrecon.ziyue.cool/
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