Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds

Bojun Ouyang, Dan Raviv

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Understanding the flow in 3D space of sparsely sampled points between two consecutive time frames is the core stone of modern geometric-driven systems such as VR/AR,Robotics,and Autonomous driving. The lack of real,non-simulated,labeled data for this task emphasizes the importance of self- or un-supervised deep architectures. This work presents a new self-supervised training method and an architecture for the 3D scene flow estimation under occlusions. Here we show that smart multi-layer fusion between flow prediction and occlusion detection outperforms traditional architectures by a large margin for occluded and non-occluded scenarios. We report state-of-the-art results on Flyingthings3D and KITTI datasets for both the supervised and self-supervised training.1

Original languageEnglish
Title of host publicationProceedings - 2021 International Conference on 3D Vision, 3DV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages782-791
Number of pages10
ISBN (Electronic)9781665426886
DOIs
StatePublished - 2021
Event9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom
Duration: 1 Dec 20213 Dec 2021

Publication series

NameProceedings - 2021 International Conference on 3D Vision, 3DV 2021

Conference

Conference9th International Conference on 3D Vision, 3DV 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period1/12/213/12/21

Fingerprint

Dive into the research topics of 'Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds'. Together they form a unique fingerprint.

Cite this