PR-RRN: Pairwise-Regularized Residual-Recursive Networks for Non-rigid Structure-from-Motion

• URL: http://arxiv.org/abs/2108.07506v1
• Date: Tue, 17 Aug 2021 08:39:02 GMT
• Title: PR-RRN: Pairwise-Regularized Residual-Recursive Networks for Non-rigid Structure-from-Motion
• Authors: Haitian Zeng, Yuchao Dai, Xin Yu, Xiaohan Wang, Yi Yang
• Abstract summary: PR-RRN is a novel neural-network based method for Non-rigid Structure-from-Motion. We propose two new pairwise regularizations to further regularize the reconstruction. Our approach achieves state-of-the-art performance on CMU MOCAP and PASCAL3D+ dataset.
• Score: 58.75694870260649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose PR-RRN, a novel neural-network based method for Non-rigid Structure-from-Motion (NRSfM). PR-RRN consists of Residual-Recursive Networks (RRN) and two extra regularization losses. RRN is designed to effectively recover 3D shape and camera from 2D keypoints with novel residual-recursive structure. As NRSfM is a highly under-constrained problem, we propose two new pairwise regularization to further regularize the reconstruction. The Rigidity-based Pairwise Contrastive Loss regularizes the shape representation by encouraging higher similarity between the representations of high-rigidity pairs of frames than low-rigidity pairs. We propose minimum singular-value ratio to measure the pairwise rigidity. The Pairwise Consistency Loss enforces the reconstruction to be consistent when the estimated shapes and cameras are exchanged between pairs. Our approach achieves state-of-the-art performance on CMU MOCAP and PASCAL3D+ dataset.

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