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DA360 model enhances 360-degree depth estimation with scale invariance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DA360, a panoramic adaptation of the Depth Anything V2 model, to improve 360-degree depth estimation. This new framework leverages the existing DAV2 model's zero-shot generalization capabilities and incorporates a lightweight adaptation process. DA360 learns a per-image shift from the ViT class token for scale-invariant supervision and integrates circular padding into the DPT decoder to eliminate seam artifacts, resulting in more accurate 3D point clouds and improved spatial coherence. AI

IMPACT Enhances 3D scene understanding for robotics and AR/VR applications by improving panoramic depth estimation accuracy.

RANK_REASON This is a research paper detailing a new model adaptation for panoramic depth estimation. [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 →

DA360 model enhances 360-degree depth estimation with scale invariance

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This is a research paper detailing a new model adaptation for panoramic depth estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hualie Jiang, Ziyang Song, Zhiqiang Lou, Rui Xu, Minglang Tan ·

    Depth Anything in $360^\circ$: Towards Scale Invariance in the Wild

    arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360$^\circ$ scene geometry, being essential for robotics and AR/VR applications. While perspective depth models have achieved remarkable zero-shot generalization via large-scale t…

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