RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction

• URL: http://arxiv.org/abs/2512.23437v1
• Date: Mon, 29 Dec 2025 12:57:19 GMT
• Title: RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction
• Authors: Shuhong Liu, Chenyu Bao, Ziteng Cui, Yun Liu, Xuangeng Chu, Lin Gu, Marcos V. Conde, Ryo Umagami, Tomohiro Hashimoto, Zijian Hu, Tianhan Xu, Yuan Gan, Yusuke Kurose, Tatsuya Harada,
• Abstract summary: RealX3D groups corruptions into four families, including illumination, scattering, and blurring, and captures each at multiple severity levels.<n>Each scene includes high-resolution capture, RAW images, and dense laser scans, from which we derive world-scale meshes and metric depth.
• Score: 51.07270086169647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce RealX3D, a real-capture benchmark for multi-view visual restoration and 3D reconstruction under diverse physical degradations. RealX3D groups corruptions into four families, including illumination, scattering, occlusion, and blurring, and captures each at multiple severity levels using a unified acquisition protocol that yields pixel-aligned LQ/GT views. Each scene includes high-resolution capture, RAW images, and dense laser scans, from which we derive world-scale meshes and metric depth. Benchmarking a broad range of optimization-based and feed-forward methods shows substantial degradation in reconstruction quality under physical corruptions, underscoring the fragility of current multi-view pipelines in real-world challenging environments.

Related papers

• NOVA3R: Non-pixel-aligned Visual Transformer for Amodal 3D Reconstruction [99.52487968452198]
  NOVA3R is an effective approach for non-pixel-aligned 3D reconstruction from a set of unposed images in a feed-forward manner.<n>It produces physically plausible geometry with fewer duplicated structures in overlapping regions.<n>It outperforms state-of-the-art methods in terms of reconstruction accuracy and completeness.
  arXiv  Detail & Related papers  (2026-03-04T15:36:25Z)
• MapAnything: Universal Feed-Forward Metric 3D Reconstruction [63.79151976126576]
  MapAnything ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, depth, or partial reconstructions.<n>It then directly regresses the metric 3D scene geometry and cameras.<n>MapAnything addresses a broad range of 3D vision tasks in a single feed-forward pass.
  arXiv  Detail & Related papers  (2025-09-16T18:00:14Z)
• RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditions [67.48495052903534]
  We propose a general and efficient multi-view feature enhancement module, RobustGS.<n>It substantially improves the robustness of feedforward 3DGS methods under various adverse imaging conditions.<n>The RobustGS module can be seamlessly integrated into existing pretrained pipelines in a plug-and-play manner.
  arXiv  Detail & Related papers  (2025-08-05T04:50:29Z)
• R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision [9.961452710097685]
  3D Low-Level Vision (3D LLV) extends classical 2D Low-Level Vision tasks into the 3D spatial domain.<n>Recent methods that integrate LLV into neural rendering frameworks are categorized to illustrate how they enable high-fidelity 3D reconstruction under adverse conditions.<n>This work positions 3D LLV as a fundamental direction for robust 3D content generation and scene-level reconstruction in real-world environments.
  arXiv  Detail & Related papers  (2025-06-19T12:25:46Z)
• MUSt3R: Multi-view Network for Stereo 3D Reconstruction [11.61182864709518]
  We propose an extension of DUSt3R from pairs to multiple views, that addresses all aforementioned concerns.<n>We entail the model with a multi-layer memory mechanism which allows to reduce the computational complexity.<n>The framework is designed to perform 3D reconstruction both offline and online, and hence can be seamlessly applied to SfM and visual SLAM scenarios.
  arXiv  Detail & Related papers  (2025-03-03T15:36:07Z)
• Direct and Explicit 3D Generation from a Single Image [25.207277983430608]
  We introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images. We incorporate epipolar attention into the latent-to-pixel decoder for pixel-level multi-view consistency. By back-projecting the generated depth pixels into 3D space, we create a structured 3D representation.
  arXiv  Detail & Related papers  (2024-11-17T03:14:50Z)
• 3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface [8.824340350342512]
  3DFIRES is a novel system for scene-level 3D reconstruction from posed images. We show it matches the efficacy of single-view reconstruction methods with only one input.
  arXiv  Detail & Related papers  (2024-03-13T17:59:50Z)
• High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
  We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
  arXiv  Detail & Related papers  (2022-11-28T18:59:52Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)

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.