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Found 3 papers, 1 papers with code
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements.
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion
In practice, the proposed BEV@DC model comprehensively takes advantage of LiDARs with rich geometric details in training, employing an enhanced depth completion manner in inference, which takes only images (RGB and depth) as input.
Dynamic Spatial Propagation Network for Depth Completion
Our solution is to estimate independent affinity matrices in each SPN iteration, but it is over-parameterized and heavy calculation.
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