Dual-View 3D Reconstruction via Learning Correspondence and Dependency of Point Cloud Regions

Author:

Jia Xin1,Yang Shourui1ORCID,Wang Yunbo2ORCID,Zhang Jianhua1ORCID,Peng Yuxin2ORCID,Chen Shengyong1ORCID

Affiliation:

1. Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China

2. Wangxuan Institute of Computer Technology, Peking University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference58 articles.

1. P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo

2. MVSNet: Depth Inference for Unstructured Multi-view Stereo

3. Learning to infer implicit surfaces without 3D supervision;liu;Proc Conf Workshop Neural Inf Process Syst (NeurlPS),2019

4. 3D-GMNet: Single-view 3D shape recovery as a Gaussian mixture;yamashita;Proc Brit Mach Vis Conf (BMVC),2020

5. Learning Shape Templates With Structured Implicit Functions

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1. PU-Mask: 3D Point Cloud Upsampling via an Implicit Virtual Mask;IEEE Transactions on Circuits and Systems for Video Technology;2024-07

2. Perceptual point cloud qality assessment for immersive metaverse experience;Digital Communications and Networks;2024-07

3. SISIM: statistical information similarity-based point cloud quality assessment;The Visual Computer;2024-03-28

4. MVGR: Mean-Variance Minimization Global Registration Method for Multiview Point Cloud in Robot Inspection;IEEE Transactions on Instrumentation and Measurement;2024

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