2018 CVPR CVPR 2018

Structure From Recurrent Motion: From Rigidity to Recurrency

Abstract

This paper proposes a new method for Non-rigidstructure-from-motion (NRSfM). Departing significantlyfrom the traditional idea of using linear low-order shapemodel for NRSfM, our method exploits the property of shaperecurrence (i.e. many dynamic shapes tend to repeat them-selves in time). We show that recurrency is in fact agen-eralized rigidity. Based on this, we show how to reduceNRSfM problems to rigid ones, provided that the recurrencecondition is satisfied. Given such a reduction, standardrigid-SFM techniques can be applied directly (without anychange) to reconstruct the non-rigid dynamic shape. To im-plement this idea as a practical approach, this paper de-velops efficient and reliable algorithm for automatic recur-rence detection, as well as new method for camera viewsclustering via rigidity-check. Experiments on both syntheticsequences and real data demonstrate the effectiveness of theproposed method. Since the method provides novel perspec-tive to look at Structure-from-Motion, we hope it will inspireother new researches in the field.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Computer Vision and Mathematics & Optimization
🧭 Keyword Pioneer — shape recurrency
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio