Affostruction: 3D Affordance Grounding with Generative Reconstruction

• URL: http://arxiv.org/abs/2601.09211v1
• Date: Wed, 14 Jan 2026 06:33:12 GMT
• Title: Affostruction: 3D Affordance Grounding with Generative Reconstruction
• Authors: Chunghyun Park, Seunghyeon Lee, Minsu Cho,
• Abstract summary: We propose Affostruction, a generative framework that reconstructs complete geometry from partial observations and grounds affordances on the full shape.<n>Affostruction achieves 19.1 aIoU on affordance grounding and 32.67 IoU for 3D reconstruction, enabling accurate affordance prediction on complete shapes.
• Score: 50.586896835836164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the problem of affordance grounding from RGBD images of an object, which aims to localize surface regions corresponding to a text query that describes an action on the object. While existing methods predict affordance regions only on visible surfaces, we propose Affostruction, a generative framework that reconstructs complete geometry from partial observations and grounds affordances on the full shape including unobserved regions. We make three core contributions: generative multi-view reconstruction via sparse voxel fusion that extrapolates unseen geometry while maintaining constant token complexity, flow-based affordance grounding that captures inherent ambiguity in affordance distributions, and affordance-driven active view selection that leverages predicted affordances for intelligent viewpoint sampling. Affostruction achieves 19.1 aIoU on affordance grounding (40.4\% improvement) and 32.67 IoU for 3D reconstruction (67.7\% improvement), enabling accurate affordance prediction on complete shapes.

Related papers

• Faithful Contouring: Near-Lossless 3D Voxel Representation Free from Iso-surface [49.742538510885]
  We propose Faithful Contouring, a voxelized representation that achieves near-lossless fidelity in 3D meshes.<n>The proposed method also shows flexibility and improvement for existing representation.
  arXiv  Detail & Related papers  (2025-11-06T03:56:12Z)
• ReconViaGen: Towards Accurate Multi-view 3D Object Reconstruction via Generation [28.308731720451053]
  We propose ReconViaGen to integrate reconstruction priors into the generative framework.<n>Our experiments demonstrate that our ReconViaGen can reconstruct complete and accurate 3D models consistent with input views in both global structure and local details.
  arXiv  Detail & Related papers  (2025-10-27T13:15:06Z)
• G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior [53.762256749551284]
  We identify accurate geometry as the fundamental prerequisite for effectively exploiting generative models to enhance 3D scene reconstruction.<n>We incorporate this geometry guidance throughout the generative pipeline to improve visibility mask estimation, guide novel view selection, and enhance multi-view consistency when inpainting with video diffusion models.<n>Our method naturally supports single-view inputs and unposed videos, with strong generalizability in both indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2025-10-14T03:06:28Z)
• Zero-P-to-3: Zero-Shot Partial-View Images to 3D Object [55.93553895520324]
  We propose a novel training-free approach that integrates local dense observations and multi-source priors for reconstruction.<n>Our method introduces a fusion-based strategy to effectively align these priors in DDIM sampling, thereby generating multi-view consistent images to supervise invisible views.
  arXiv  Detail & Related papers  (2025-05-29T03:51:37Z)
• Decompositional Neural Scene Reconstruction with Generative Diffusion Prior [64.71091831762214]
  Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture, is intriguing for downstream applications.<n>Recent approaches incorporate semantic or geometric regularization to address this issue, but they suffer significant degradation in underconstrained areas.<n>We propose DP-Recon, which employs diffusion priors in the form of Score Distillation Sampling (SDS) to optimize the neural representation of each individual object under novel views.
  arXiv  Detail & Related papers  (2025-03-19T02:11:31Z)
• PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction [49.7580491592023]
  We propose PVP-Recon, a novel and effective sparse-view surface reconstruction method. PVP-Recon starts initial surface reconstruction with as few as 3 views and progressively adds new views. This progressive view planning progress is interleaved with a neural SDF-based reconstruction module.
  arXiv  Detail & Related papers  (2024-09-09T10:06:34Z)
• Point Scene Understanding via Disentangled Instance Mesh Reconstruction [21.92736190195887]
  We propose aDisentangled Instance Mesh Reconstruction (DIMR) framework for effective point scene understanding. A segmentation-based backbone is applied to reduce false positive object proposals. We leverage a mesh-aware latent code space to disentangle the processes of shape completion and mesh generation.
  arXiv  Detail & Related papers  (2022-03-31T06:36:07Z)
• RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction [19.535169371240073]
  We introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from point clouds. We decouple the instance reconstruction into global object localization and local shape prediction. Our approach consistently outperforms the state-of-the-arts and improves over 11 of mesh IoU in object reconstruction.
  arXiv  Detail & Related papers  (2020-11-30T12:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.