SLIM adapts MoGe-2 to truly sparse LiDAR via partial-convolution encoder and multi-scale fusion neck, cutting absolute relative depth error by 39-51% at 100-150 m on Virtual KITTI and CARLA under density-agnostic training.
Lianget al.Distilling Monocular Foundation Model for Fine-grained Depth Completion (DMD3C)
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Sparse-LiDAR Prompting of Monocular Geometry Foundations: An Empirical Study Toward Long-Range Driving Depth
SLIM adapts MoGe-2 to truly sparse LiDAR via partial-convolution encoder and multi-scale fusion neck, cutting absolute relative depth error by 39-51% at 100-150 m on Virtual KITTI and CARLA under density-agnostic training.