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Found 3 papers, 1 papers with code
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.
Geometry-Aware Network for Domain Adaptive Semantic Segmentation
Moreover, to improve the 2D classifier in the target domain, we perform domain-invariant geometric adaptation from source to target and unify the 2D semantic and 3D geometric segmentation results in two domains.
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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