DepthMaster unifies metric monocular depth estimation for perspective and panoramic images by patching panoramas into perspective views, adding a consistency loss and virtual cameras, and training mostly on perspective data to reach SOTA zero-shot results on 13 datasets.
Flow-motion and depth network for monocular stereo and beyond
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One transformer, trained with random-sized temporal attention chunks, unifies offline, streaming, and long-video depth, normal, and point-map estimation and reports new best numbers on five public benchmarks.
SCOPE uses affine-invariant 3D point maps with shared parameters and three consistency innovations to estimate 3D geometry from extended monocular videos, reporting 24.2% and 34.9% error reductions on ScanNet.
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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DepthMaster: Unified Monocular Depth Estimation for Perspective and Panoramic Images
DepthMaster unifies metric monocular depth estimation for perspective and panoramic images by patching panoramas into perspective views, adding a consistency loss and virtual cameras, and training mostly on perspective data to reach SOTA zero-shot results on 13 datasets.
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Towards Consistent Video Geometry Estimation
One transformer, trained with random-sized temporal attention chunks, unifies offline, streaming, and long-video depth, normal, and point-map estimation and reports new best numbers on five public benchmarks.
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SCOPE: Scale-Consistent One-Pass Estimation of 3D Geometry
SCOPE uses affine-invariant 3D point maps with shared parameters and three consistency innovations to estimate 3D geometry from extended monocular videos, reporting 24.2% and 34.9% error reductions on ScanNet.
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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.