Joint Depth Prediction and Semantic Segmentation with Multi-View SAM
Mykhailo Shvets Dongxu Zhao Marc Niethammer Roni Sengupta Alexander C. Berg
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.