RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer

• URL: http://arxiv.org/abs/2205.01568v1
• Date: Tue, 3 May 2022 15:43:57 GMT
• Title: RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer
• Authors: Bayram Bayramli, Junhwa Hur, Hongtao Lu
• Abstract summary: We introduce a self-supervised monocular scene flow method that substantially improves the accuracy over the previous approaches. Based on RAFT, a state-of-the-art optical flow model, we design a new decoder to iteratively update 3D motion fields and disparity maps simultaneously. Our method achieves state-of-the-art accuracy among all self-supervised monocular scene flow methods, improving accuracy by 34.2%.
• Score: 21.125470798719967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning scene flow from a monocular camera still remains a challenging task due to its ill-posedness as well as lack of annotated data. Self-supervised methods demonstrate learning scene flow estimation from unlabeled data, yet their accuracy lags behind (semi-)supervised methods. In this paper, we introduce a self-supervised monocular scene flow method that substantially improves the accuracy over the previous approaches. Based on RAFT, a state-of-the-art optical flow model, we design a new decoder to iteratively update 3D motion fields and disparity maps simultaneously. Furthermore, we propose an enhanced upsampling layer and a disparity initialization technique, which overall further improves accuracy up to 7.2%. Our method achieves state-of-the-art accuracy among all self-supervised monocular scene flow methods, improving accuracy by 34.2%. Our fine-tuned model outperforms the best previous semi-supervised method with 228 times faster runtime. Code will be publicly available.

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