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2023

SAD: Segment Any RGBD

Cen, Jun, Wu, Yizheng, Wang, Kewei et al.

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The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images.

However, SAM exhibits a stronger emphasis on texture information while paying less attention to geometry information when segmenting RGB images.
To address this limitation, we propose the Segment Any RGBD (SAD) model, which is specifically designed to extract geometry information directly from images.
Inspired by the natural ability of humans to identify objects through the visualization of depth maps, SAD utilizes SAM to segment the rendered depth map, thus providing cues with enhanced geometry information and mitigating the issue of over-segmentation.

SAD: Segment Any RGBD · Around

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Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data
Y.-T. Hu, J. Wang, R. A. Yeh, and A. G. Schwing · 2021
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