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arXiv Computer Vision By Qirui Wu, Stan Birchfield, Hesam Rabeti, Angel X. Chang, Bowen Wen

GATOR: Generative and Agentic 3D Object Reconstruction From Casual Images

Read the original on arXiv Computer Vision →

GATOR is a generative and agentic framework that reconstructs complete, scene‑aligned 3D objects from casual images. It integrates sparse, uncertain observations by mixing patch‑aligned RGB, target‑mask, and pointmap features, and uses text‑guided semantic conditioning to inform structure, geometry, and appearance generation. The framework then employs a multimodal agent to refine the asset through an observation‑guided edit‑render‑review loop, achieving strong geometric and appearance fidelity across synthetic, cluttered, and indoor scenes while recovering scene‑relative pose from sparse inputs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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