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New CoTL framework learns compact 3D scene tokens from multi-view images

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CoTL, a novel framework designed for learning compact token representations of 3D scenes from multi-view images. This method compresses input images into a small set of latent tokens that aggregate information across different views, forming a scene representation that grows with available observations. A query-based Gaussian decoder then uses these tokens to reconstruct explicit 3D Gaussian primitives, enabling competitive novel-view synthesis and serving as a latent space for generative 3D scene models. AI

IMPACT This research could lead to more efficient methods for storing and generating 3D scene data, impacting fields like virtual reality and content creation.

RANK_REASON The cluster contains an academic paper detailing a new method for 3D scene representation. [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 →

New CoTL framework learns compact 3D scene tokens from multi-view images

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The cluster contains an academic paper detailing a new method for 3D scene representation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Han Liang, Sibo Wu, Anpei Chen, R\"udiger Westermann ·

    CoTL: Compact Token Learning for 3D Scene Representation

    arXiv:2610.18446v2 Announce Type: replace Abstract: We present CoTL, a framework that learns a compact set of tokens that preserves scene geometry and appearance from unposed multi-view images. Our encoder compresses the input images into small per-view sets of latent tokens that…

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