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3D-FUTURE: 3D Furniture shape with TextURE

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arxiv 2009.09633 v1 pith:Y5W6PGUB submitted 2020-09-21 cs.CV

classification cs.CV
keywords d-futurefurnitureshapestextureshapelessobjectrecovery
verification ladder T0 review T1 audit T2 compute T3 formal
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The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textures, making them less attractive for comprehensive and subtle research in areas such as high-quality 3D mesh and texture recovery. This paper presents 3D Furniture shape with TextURE (3D-FUTURE): a richly-annotated and large-scale repository of 3D furniture shapes in the household scenario. At the time of this technical report, 3D-FUTURE contains 20,240 clean and realistic synthetic images of 5,000 different rooms. There are 9,992 unique detailed 3D instances of furniture with high-resolution textures. Experienced designers developed the room scenes, and the 3D CAD shapes in the scene are used for industrial production. Given the well-organized 3D-FUTURE, we provide baseline experiments on several widely studied tasks, such as joint 2D instance segmentation and 3D object pose estimation, image-based 3D shape retrieval, 3D object reconstruction from a single image, and texture recovery for 3D shapes, to facilitate related future researches on our database.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GOPI: Generation-Oriented 3D Pose Inference for Furniture Insertion from Single-View RGB-D Indoor Scenes

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A pose-first furniture insertion framework infers 3D placement from masked RGB-D input and uses its image-plane projection to condition diffusion, improving geometric feasibility on a synthetic 3D-FRONT benchmark.

  2. TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis

    cs.CV 2025-09 conditional novelty 6.0 of 10

    TRELLIS-derived surface features improve aneurysm classification, segmentation, and hemodynamic simulation, including a 15% lower blood-flow prediction error.

  3. 3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D Shapes

    cs.CV 2025-01 conditional novelty 6.0 of 10

    3DCoMPaT200 expands compositional part-material 3D understanding to 200 shape categories and adds a text-based compositional shape retrieval benchmark.

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