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.
Prompting depth anything for 4k resolution accurate metric depth estimation
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MoGe-2 recovers metric-scale 3D point maps with fine details from single images via data refinement and extension of affine-invariant predictions.
citing papers explorer
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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.
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MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
MoGe-2 recovers metric-scale 3D point maps with fine details from single images via data refinement and extension of affine-invariant predictions.