Refine Any Object in Any Scene

• URL: http://arxiv.org/abs/2506.23835v1
• Date: Mon, 30 Jun 2025 13:26:21 GMT
• Title: Refine Any Object in Any Scene
• Authors: Ziwei Chen, Ziling Liu, Zitong Huang, Mingqi Gao, Feng Zheng,
• Abstract summary: Refine Any object In any ScenE (RAISE) is a novel 3D enhancement framework that recovers fine-grained object geometry and appearance under missing views.<n>RAISE progressively refines geometry and texture by aligning each proxy to its degraded counterpart in 7-DOF pose.<n> experiments on challenging benchmarks show that RAISE significantly outperforms state-of-the-art methods in both novel view synthesis and geometry completion tasks.
• Score: 39.109559659959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Viewpoint missing of objects is common in scene reconstruction, as camera paths typically prioritize capturing the overall scene structure rather than individual objects. This makes it highly challenging to achieve high-fidelity object-level modeling while maintaining accurate scene-level representation. Addressing this issue is critical for advancing downstream tasks requiring detailed object understanding and appearance modeling. In this paper, we introduce Refine Any object In any ScenE (RAISE), a novel 3D enhancement framework that leverages 3D generative priors to recover fine-grained object geometry and appearance under missing views. Starting from substituting degraded objects with proxies, via a 3D generative model with strong 3D understanding, RAISE progressively refines geometry and texture by aligning each proxy to its degraded counterpart in 7-DOF pose, followed by correcting spatial and appearance inconsistencies via registration-constrained enhancement. This two-stage refinement ensures the high-fidelity geometry and appearance of the original object in unseen views while maintaining consistency in spatial positioning, observed geometry, and appearance. Extensive experiments on challenging benchmarks show that RAISE significantly outperforms state-of-the-art methods in both novel view synthesis and geometry completion tasks. RAISE is made publicly available at https://github.com/PolySummit/RAISE.

Related papers

• Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations [112.29763628638112]
  Object-X is a versatile multi-modal 3D representation framework.<n>It can encoding rich object embeddings and decoding them back into geometric and visual reconstructions.<n>It supports a range of downstream tasks, including scene alignment, single-image 3D object reconstruction, and localization.
  arXiv  Detail & Related papers  (2025-06-05T09:14:42Z)
• HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation [50.206100327643284]
  HiScene is a novel hierarchical framework that bridges the gap between 2D image generation and 3D object generation.<n>We generate 3D content that aligns with 2D representations while maintaining compositional structure.
  arXiv  Detail & Related papers  (2025-04-17T16:33:39Z)
• REArtGS: Reconstructing and Generating Articulated Objects via 3D Gaussian Splatting with Geometric and Motion Constraints [48.80178020541189]
  REArtGS is a novel framework that introduces additional geometric and motion constraints to 3D Gaussian primitives.<n>We establish deformable fields for 3D Gaussians constrained by the kinematic structures of articulated objects, achieving unsupervised generation of surface meshes in unseen states.
  arXiv  Detail & Related papers  (2025-03-09T16:05:36Z)
• CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image [44.8172828045897]
  Current methods often struggle with domain-specific limitations or low-quality object generation.<n>We propose CAST, a novel method for 3D scene reconstruction and recovery.
  arXiv  Detail & Related papers  (2025-02-18T14:29:52Z)
• Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion [16.379647695019308]
  3D scene reconstruction is a foundational problem in computer vision.<n>We introduce the Gaussian Object Carver (GOC), a novel, efficient, and scalable framework for object-compositional 3D scene reconstruction.<n>GOC leverage 3D Gaussian Splatting (GS), enriched with monocular geometry priors and multi-view geometry regularization, to achieve high-quality and flexible reconstruction.
  arXiv  Detail & Related papers  (2024-12-03T01:34:39Z)
• Zero-Shot Multi-Object Scene Completion [59.325611678171974]
  We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-21T17:59:59Z)
• Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture [47.44029968307207]
  We propose a novel framework for simultaneous high-fidelity recovery of object shapes and textures from single-view images. Our approach utilizes the proposed Single-view neural implicit Shape and Radiance field (SSR) representations to leverage both explicit 3D shape supervision and volume rendering. A distinctive feature of our framework is its ability to generate fine-grained textured meshes while seamlessly integrating rendering capabilities into the single-view 3D reconstruction model.
  arXiv  Detail & Related papers  (2023-11-01T11:46:15Z)
• Single-view 3D Mesh Reconstruction for Seen and Unseen Categories [69.29406107513621]
  Single-view 3D Mesh Reconstruction is a fundamental computer vision task that aims at recovering 3D shapes from single-view RGB images. This paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories. We propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction.
  arXiv  Detail & Related papers  (2022-08-04T14:13:35Z)

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.