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DefVINS pipeline tackles deformable scenes in visual-inertial odometry

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DefVINS, a novel visual-inertial odometry pipeline specifically designed for deformable environments. Unlike traditional methods that assume rigidity, DefVINS models the odometry state by separating a rigid, IMU-anchored component from a non-rigid scene warp represented by a deformation graph. The team also introduced VIMandala, the first benchmark dataset featuring real images and ground-truth camera poses for deformable visual-inertial odometry, alongside enhancements to the synthetic Drunkard's benchmark. Experiments on these benchmarks demonstrate DefVINS' superior performance compared to existing rigid visual-inertial and non-rigid visual odometry baselines. AI

IMPACT This research advances robotics by enabling more accurate navigation in complex, non-rigid environments.

RANK_REASON The cluster contains an arXiv paper detailing a new method and benchmark for visual-inertial odometry. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DefVINS pipeline tackles deformable scenes in visual-inertial odometry

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The cluster contains an arXiv paper detailing a new method and benchmark for visual-inertial odometry. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Samuel Cerezo, Javier Civera ·

    DefVINS: Visual-Inertial Odometry for Deformable Scenes

    arXiv:2601.00702v3 Announce Type: replace-cross Abstract: Deformable scenes violate the rigidity assumptions underpinning classical visual--inertial odometry (VIO), often leading to over-fitting to local non-rigid motion or to severe camera pose drift when deformation dominates v…

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