Masked Depth Modeling for Spatial Perception
Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D environments. Capturing pixel-aligned metric depth using RGB-D cameras would be the most viable way, yet it usually faces obstacles posed by hardware limitations and challenging imaging conditions, especially in the presence of specular or texture-less surfaces. In this work, we argue that the inaccuracies from depth sensors can be viewed as "masked" signals that inherently reflect underlying geometric ambiguities. Building on this motivation, we present LingBot-Depth, a depth completion model which leverages visual context to refine depth maps through masked depth modeling and incorporates an automated data curation pipeline for scalable training. It is encouraging to see that our model outperforms top-tier RGB-D cameras in terms of both depth precision and pixel coverage. Experimental results on a range of downstream tasks further suggest that LingBot-Depth offers an aligned latent representation across RGB and depth modalities. We release the code, checkpoint, and 3M RGB-depth pairs (including 2M real data and 1M simulated data) to the community of spatial perception.
69781d38026bdf0473116d7f
Bin Tan
69781d38026bdf0473116d80
Changjiang Sun
69781d38026bdf0473116d81
Xiage Qin
69781d38026bdf0473116d82
Hanat Adai
69781d38026bdf0473116d83
Zelin Fu
69781d38026bdf0473116d84
Tianxiang Zhou
69781d38026bdf0473116d85
Han Zhang
69781d38026bdf0473116d86
Yinghao Xu
claimed_verified
2026-02-04T12:34:13.671Z
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/avatars/ac58a796bb54f334fdb475a4b75c4d27.svg
Yinghao Xu
justimyhxu
user
justimyhxu
69781d38026bdf0473116d87
Xing Zhu
69781d38026bdf0473116d88
Yujun Shen
69781d38026bdf0473116d89
Nan Xue
Masked Depth Modeling for Spatial Perception
Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D environments. Capturing pixel-aligned metric depth using RGB-D cameras would be the most viable way, yet it usually faces obstacles posed by hardware limitations and challenging imaging conditions, especially in the presence of specular or texture-less surfaces. In this work, we argue that the inaccuracies from depth sensors can be viewed as "masked" signals that inherently reflect underlying geometric ambiguities. Building on this motivation, we present LingBot-Depth, a depth completion model which leverages visual context to refine depth maps through masked depth modeling and incorporates an automated data curation pipeline for scalable training. It is encouraging to see that our model outperforms top-tier RGB-D cameras in terms of both depth precision and pixel coverage. Experimental results on a range of downstream tasks further suggest that LingBot-Depth offers an aligned latent representation across RGB and depth modalities. We release the code, checkpoint, and 3M RGB-depth pairs (including 2M real data and 1M simulated data) to the community of spatial perception.
LingBot-Depth is a depth completion model that uses visual context to refine depth maps through masked depth modeling and automated data curation for improved spatial perception in robotics and autonomous systems.
depth completion masked depth modeling visual context automated data curation RGB-D cameras spatial perception latent representation