Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation

Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli, Deqing Sun, James M. Rehg, Jan Kautz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems. In real-world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasing the task complexity because the scene is observed from different viewpoints. The primary challenge is the disambiguation of the camera motion from scene motion, which becomes more difficult as the amount of rigidity observed decreases, even with successful estimation of 2D image correspondences. Compared to other state-of-the-art 3D scene flow estimation methods, in this paper, we propose to learn the rigidity of a scene in a supervised manner from an extensive collection of dynamic scene data, and directly infer a rigidity mask from two sequential images with depths. With the learned network, we show how we can effectively estimate camera motion and projected scene flow using computed 2D optical flow and the inferred rigidity mask. For training and testing the rigidity network, we also provide a new semi-synthetic dynamic scene dataset (synthetic foreground objects with a real background) and an evaluation split that accounts for the percentage of observed non-rigid pixels. Through our evaluation, we show the proposed framework outperforms current state-of-the-art scene flow estimation methods in challenging dynamic scenes.

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsVittorio Ferrari, Cristian Sminchisescu, Martial Hebert, Yair Weiss
PublisherSpringer
Pages484-501
Number of pages18
ISBN (Print)9783030012274
DOIs
StatePublished - 2018
Externally publishedYes
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: Sep 8 2018Sep 14 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11209 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Computer Vision, ECCV 2018
Country/TerritoryGermany
CityMunich
Period9/8/189/14/18

Keywords

  • Dynamic scene analysis
  • Motion segmentation
  • Rigidity estimation
  • Scene flow

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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