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RayTun3R adapts 3D foundation models for fisheye cameras

Researchers have developed RayTun3R, a novel method to adapt existing 3D foundation models for use with fisheye camera imagery. These models, which typically perform well with standard pinhole cameras, degrade significantly when presented with the wider field of view from fisheye lenses. RayTun3R addresses this by making minimal adjustments to lightweight components of the pretrained models, specifically those related to positional encoding and prediction-grid coordinates, while keeping the core network fixed. This parameter-efficient approach, requiring only 10,752 trainable parameters, can be learned quickly and applied to subsequent frames without increasing inference time, leading to substantial reductions in rotation error. AI

IMPACT Enables wider application of advanced 3D vision models to datasets captured with fisheye lenses.

RANK_REASON Academic paper detailing a new method for adapting existing models. [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 →

RayTun3R adapts 3D foundation models for fisheye cameras

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Daniil Sinitsyn, Nikita Araslanov, Daniel Cremers ·

    RayTun3R: Online Camera Adaptation in 3D Foundation Models

    arXiv:2607.02711v1 Announce Type: new Abstract: Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, $\pi^3$, and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole imagery, but degrade sharply under fisheye camera geometry. We show that this …