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pith:Y3D33KXK

pith:2025:Y3D33KXKMQJS2JSA3BSX6HB7KO
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ViPE: Video Pose Engine for 3D Geometric Perception

Aleksandr Korovko, Chen-Hsuan Lin, Dmitry Slepichev, Hesam Rabeti, Huan Ling, Jiahui Huang, Jiawei Ren, Joydeep Biswas, Jun Gao, Kevin Xie, Laura Leal-Taixe, Qunjie Zhou, Sanja Fidler, Tianchang Shen, Xuanchi Ren

ViPE estimates camera poses and near-metric depth maps from any raw video without calibration.

arxiv:2508.10934 v1 · 2025-08-12 · cs.CV · cs.GR · cs.RO · eess.IV

Record completeness

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4 Citations open
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Claims

C1strongest claim

ViPE outperforms existing uncalibrated pose estimation baselines by 18%/50% on TUM/KITTI sequences and annotates approximately 96M frames with accurate camera poses and dense depth maps.

C2weakest assumption

That the engine produces reliable near-metric depth and accurate poses on diverse in-the-wild videos without per-video calibration or ground-truth supervision.

C3one line summary

ViPE estimates camera intrinsics, motion, and dense near-metric depth from uncalibrated videos, outperforming baselines on TUM and KITTI while releasing annotations for 96M frames across real and generated videos.

References

91 extracted · 91 resolved · 9 Pith anchors

[1] Cosmos World Foundation Model Platform for Physical AI 2025 · arXiv:2501.03575
[2] Cosmos-transfer1: Conditional world generation with adaptive multimodal control 2025
[3] L4P: Low-level 4D vision perception unified
[4] ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data 2021 · arXiv:2111.08897
[5] Depth Pro: Sharp Monocular Metric Depth in Less Than a Second 2024 · arXiv:2410.02073

Formal links

1 machine-checked theorem link

Cited by

21 papers in Pith

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SignaturePith Ed25519 (pith-v1-2026-05) · public key
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c6c7bdaaea64132d2640d8657f1c3f539091f4f0ee4485b353e223441288fd3d

Aliases

arxiv: 2508.10934 · arxiv_version: 2508.10934v1 · doi: 10.48550/arxiv.2508.10934
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Canonical record JSON
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