Estimating Depth from RGB and Sparse Sensing

Zhao Chen, Vijay Badrinarayanan, Gilad Drozdov, Andrew Rabinovich

Abstract: We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both indoor/outdoor scenes and produces state-of-the-art dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI datasets. We surpass the state-of-the-art for monocular depth estimation even with depth values for only 1 out of every $${\sim }10000$$ image pixels, and we outperform other sparse-to-dense depth methods at all sparsity levels. With depth values for $$1{\slash }256$$ of the image pixels, we achieve a mean error of less than $$1\%$$ of actual depth on indoor scenes, comparable to the performance of consumer-grade depth sensor hardware. Our experiments demonstrate that it would indeed be possible to efficiently transform sparse depth measurements obtained using e.g. lower-power depth sensors or SLAM systems into high-quality dense depth maps.