arXiv Computer Vision
SiZeUp: Fast 3D Proxy from Aerial Images via Depth Ordinal Loss
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M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo
arXiv:2608.20788v1 Announce Type: new Abstract: Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or...
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MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
arXiv:2601. 22054v2 Announce Type: replace-cross Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data.
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ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning
arXiv:2607. 12433v1 Announce Type: cross Abstract: Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE).
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HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting
arXiv:2608. 14136v1 Announce Type: cross Abstract: Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptively capture scene content from coarse building structures to fine architectural details.
Hugging Face Trending Papers
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Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction
Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full $360^{\circ}$ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training.
Hugging Face Trending Papers
Jul 2
FoundDP: Revisiting Weak Disparity Observability in Dual-Pixel Depth Estimation
Dual-pixel (DP) imaging enables metric depth estimation from a single camera using sub-aperture disparity. However, the extremely small effective baseline limits disparity observability, leading to structural degradation and depth failure in textureless, low-contrast, or downsampled regions.