Self-Supervised Monocular Scene Flow Estimation

• URL: http://arxiv.org/abs/2004.04143v2
• Date: Wed, 15 Apr 2020 22:17:10 GMT
• Title: Self-Supervised Monocular Scene Flow Estimation
• Authors: Junhwa Hur, Stefan Roth
• Abstract summary: We propose a novel monocular scene flow method that yields competitive accuracy and real-time performance. By taking an inverse problem view, we design a single convolutional neural network (CNN) that successfully estimates depth and 3D motion simultaneously.
• Score: 27.477810324117016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene flow estimation has been receiving increasing attention for 3D environment perception. Monocular scene flow estimation -- obtaining 3D structure and 3D motion from two temporally consecutive images -- is a highly ill-posed problem, and practical solutions are lacking to date. We propose a novel monocular scene flow method that yields competitive accuracy and real-time performance. By taking an inverse problem view, we design a single convolutional neural network (CNN) that successfully estimates depth and 3D motion simultaneously from a classical optical flow cost volume. We adopt self-supervised learning with 3D loss functions and occlusion reasoning to leverage unlabeled data. We validate our design choices, including the proxy loss and augmentation setup. Our model achieves state-of-the-art accuracy among unsupervised/self-supervised learning approaches to monocular scene flow, and yields competitive results for the optical flow and monocular depth estimation sub-tasks. Semi-supervised fine-tuning further improves the accuracy and yields promising results in real-time.

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