SCORP: Scene-Consistent Object Refinement via Proxy Generation and Tuning

• URL: http://arxiv.org/abs/2506.23835v2
• Date: Mon, 22 Sep 2025 08:28:03 GMT
• Title: SCORP: Scene-Consistent Object Refinement via Proxy Generation and Tuning
• Authors: Ziwei Chen, Ziling Liu, Zitong Huang, Mingqi Gao, Feng Zheng,
• Abstract summary: We introduce Scene-Consistent Object Refinement via Proxy Generation and Tuning (SCORP)<n>SCORP is a novel 3D enhancement framework that leverages 3D generative priors to recover fine-grained object geometry and appearance under missing views.<n>It achieves consistent gains over recent state-of-the-art baselines on both novel view synthesis and geometry completion tasks.
• Score: 46.441761732998536
• 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 high-fidelity object reconstruction. In this paper, we introduce Scene-Consistent Object Refinement via Proxy Generation and Tuning (SCORP), a novel 3D enhancement framework that leverages 3D generative priors to recover fine-grained object geometry and appearance under missing views. Starting with proxy generation by substituting degraded objects using a 3D generation model, SCORP then progressively refines geometry and texture by aligning each proxy to its degraded counterpart in 7-DoF pose, followed by correcting spatial and appearance inconsistencies through registration-constrained enhancement. This two-stage proxy tuning ensures the high-fidelity geometry and appearance of the original object in unseen views while maintaining consistency in spatial positioning, observed geometry, and appearance. Across challenging benchmarks, SCORP achieves consistent gains over recent state-of-the-art baselines on both novel view synthesis and geometry completion tasks. SCORP is available at https://github.com/PolySummit/SCORP.

Related papers

• Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspective [8.276620253870338]
  Point cloud completion aims to reconstruct complete 3D shapes from partial observations.<n>Most methods still follow a Completion-by-Inpainting paradigm.<n>We propose Completion-by-Correction, which begins with a complete shape prior and performs feature-space correction to align it with the partial observation.
  arXiv  Detail & Related papers  (2025-11-15T11:51:13Z)
• IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction [82.53307702809606]
  Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions.<n>We propose InstanceGrounded Geometry Transformer (IGGT) to unify the knowledge for both spatial reconstruction and instance-level contextual understanding.
  arXiv  Detail & Related papers  (2025-10-26T14:57:44Z)
• WorldGrow: Generating Infinite 3D World [75.81531067447203]
  We tackle the challenge of generating the infinitely extendable 3D world -- large, continuous environments with coherent geometry and realistic appearance.<n>We propose WorldGrow, a hierarchical framework for unbounded 3D scene synthesis.<n>Our method features three core components: (1) a data curation pipeline that extracts high-quality scene blocks for training, making the 3D structured latent representations suitable for scene generation; (2) a 3D block inpainting mechanism that enables context-aware scene extension; and (3) a coarse-to-fine generation strategy that ensures both global layout plausibility and local geometric/textural fidelity.
  arXiv  Detail & Related papers  (2025-10-24T17:39:52Z)
• Structural Energy-Guided Sampling for View-Consistent Text-to-3D [18.973527029488746]
  Text-to-3D generation often suffers from the Janus problem, where objects collapse into duplicated or distorted geometry from other angles.<n>We propose Structural Energy-Guided Sampling (SEGS), a training-free, plug-and-play framework that enforces multi-view consistency entirely at sampling time.
  arXiv  Detail & Related papers  (2025-08-23T06:26:04Z)
• 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)
• Learning Fine-Grained Geometry for Sparse-View Splatting via Cascade Depth Loss [15.425094458647933]
  We introduce Hierarchical Depth-Guided Splatting (HDGS), a depth supervision framework that progressively refines geometry from coarse to fine levels.<n>By enforcing multi-scale depth consistency, our method substantially improves structural fidelity in sparse-view scenarios.
  arXiv  Detail & Related papers  (2025-05-28T12:16:42Z)
• 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)
• Online 3D Scene Reconstruction Using Neural Object Priors [83.14204014687938]
  This paper addresses the problem of reconstructing a scene online at the level of objects given an RGB-D video sequence.<n>We propose a feature grid mechanism to continuously update object-centric neural implicit representations as new object parts are revealed.<n>Our approach outperforms state-of-the-art neural implicit models for this task in terms of reconstruction accuracy and completeness.
  arXiv  Detail & Related papers  (2025-03-24T17:09:36Z)
• 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.