Joint Depth Prediction and Semantic Segmentation with Multi-View SAM

• URL: http://arxiv.org/abs/2311.00134v1
• Date: Tue, 31 Oct 2023 20:15:40 GMT
• Title: Joint Depth Prediction and Semantic Segmentation with Multi-View SAM
• Authors: Mykhailo Shvets, Dongxu Zhao, Marc Niethammer, Roni Sengupta, Alexander C. Berg
• Abstract summary: We propose a Multi-View Stereo (MVS) technique for depth prediction that benefits from rich semantic features of the Segment Anything Model (SAM) This enhanced depth prediction, in turn, serves as a prompt to our Transformer-based semantic segmentation decoder.
• Score: 59.99496827912684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task approaches to joint depth and segmentation prediction are well-studied for monocular images. Yet, predictions from a single-view are inherently limited, while multiple views are available in many robotics applications. On the other end of the spectrum, video-based and full 3D methods require numerous frames to perform reconstruction and segmentation. With this work we propose a Multi-View Stereo (MVS) technique for depth prediction that benefits from rich semantic features of the Segment Anything Model (SAM). This enhanced depth prediction, in turn, serves as a prompt to our Transformer-based semantic segmentation decoder. We report the mutual benefit that both tasks enjoy in our quantitative and qualitative studies on the ScanNet dataset. Our approach consistently outperforms single-task MVS and segmentation models, along with multi-task monocular methods.

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