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SceneEvolve framework reconstructs 3D scenes using code evolution

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SceneEvolve, a novel framework for reconstructing editable 3D scenes from images by representing scenes as executable code. This approach utilizes an evolutionary process to refine a 'harness' around a small open-source agent, improving its ability to reason about geometry, object structure, and spatial relationships. SceneEvolve, when combined with the Qwen3.8-27B model, significantly enhances reconstruction quality and reduces inference time compared to the base model. Notably, it outperforms the larger proprietary GPT-5.6-Sol model in reconstruction quality and deployment cost, and its evolved harness even generalizes to other models like Kimi-K3. AI

IMPACT This research could lead to more efficient and cost-effective methods for creating editable 3D content, potentially impacting fields like virtual reality, gaming, and digital twins.

RANK_REASON The cluster contains a research paper detailing a new framework and methodology for 3D scene reconstruction. [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 →

SceneEvolve framework reconstructs 3D scenes using code evolution

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The cluster contains a research paper detailing a new framework and methodology for 3D scene reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Haoyu Yao, Chenguo Lin, Ruize Du, Yadong Mu ·

    Learning to Code the World: Harness Evolution for Compositional 3D Reconstruction

    arXiv:2610.37046v1 Announce Type: new Abstract: Reconstructing editable and compositional 3D scenes from a real image requires jointly reasoning about geometry, object structure, and spatial relationships. We introduce SceneEvolve, a framework that represents 3D scenes as executa…

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