3DReflecNet: A Large-Scale Dataset for 3D Reconstruction of Reflective, Transparent, and Low-Texture Objects

• URL: http://arxiv.org/abs/2605.10204v1
• Date: Mon, 11 May 2026 08:51:28 GMT
• Title: 3DReflecNet: A Large-Scale Dataset for 3D Reconstruction of Reflective, Transparent, and Low-Texture Objects
• Abstract summary: 3DReflecNet is a large-scale hybrid dataset exceeding 22 TB.<n>This dataset spans diverse materials, complex lighting conditions, and a wide range of geometric forms.<n>We design benchmarks for five core tasks: image matching, structure-from-motion, novel view synthesis, removal, and relighting.
• Score: 41.25903201175962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 3D reconstruction of objects with reflective, transparent, or low-texture surfaces still remains notoriously challenging. Such materials often violate key assumptions in multi-view reconstruction pipelines, such as photometric consistency and the availability on distinct geometric texture cues. Existing datasets primarily focus on diffuse, textured objects, and therefore provide limited insight into performance under real-world material complexities. We introduce 3DReflecNet, a large-scale hybrid dataset exceeding 22 TB that is specifically designed to benchmark and advance 3D vision methods for these challenging materials. 3DReflecNet combines two types of data: over 120,000 synthetic instances generated via physically-based rendering of more than 12,000 shapes, and over 1,000 real-world objects captured using consumer devices. Together, these data consist of more than 7 million multi-view frames. The dataset spans diverse materials, complex lighting conditions, and a wide range of geometric forms, including shapes generated from both real and LLM-synthesized 2D images using diffusion-based pipelines. To support robust evaluation, we design benchmarks for five core tasks: image matching, structure-from-motion, novel view synthesis, reflection removal, and relighting. Extensive experiments demonstrate that state-of-the-art methods struggle to maintain accuracy across these settings, highlighting the need for more resilient 3D vision models.

Related papers

• Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations [29.241988496403383]
  RecGen is a generative framework for probabilistic joint estimation of object and part shapes.<n>It generalizes across diverse object types and real-world environments.<n> RecGen achieves state-of-the-art performance on complex, heavily occluded datasets.
  arXiv  Detail & Related papers  (2026-04-29T18:51:21Z)
• ICTPolarReal: A Polarized Reflection and Material Dataset of Real World Objects [11.716946263732746]
  We introduce a large-scale polarized reflection and material dataset of real-world objects, captured with an 8-camera, 346-light Light Stage.<n>Our dataset spans 218 everyday objects across five acquisition dimensions-multiview, multi-illumination, polarization, reflectance separation, and material attributes-yielding over 1.2M high-resolution images.<n>Using this dataset, we train and evaluate state-of-the-art inverse and forward rendering models on intrinsic decomposition, relighting, and sparse-view 3D reconstruction.
  arXiv  Detail & Related papers  (2026-03-26T01:01:18Z)
• LARM: A Large Articulated-Object Reconstruction Model [29.66486888001511]
  LARM is a unified feedforward framework that reconstructs 3D articulated objects from sparse-view images.<n>LARM generates auxiliary outputs such as depth maps and part masks to facilitate explicit 3D mesh extraction and joint estimation.<n>Our pipeline eliminates the need for dense supervision and supports high-fidelity reconstruction across diverse object categories.
  arXiv  Detail & Related papers  (2025-11-14T18:55:27Z)
• Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations [114.57192386025373]
  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)
• IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations [64.07859467542664]
  Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics.<n>Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs.<n>We introduce IDArb, a diffusion-based model designed to perform intrinsic decomposition on an arbitrary number of images under varying illuminations.
  arXiv  Detail & Related papers  (2024-12-16T18:52:56Z)
• Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics [50.23625950905638]
  We present a new dataset for textured mesh saliency, created through an innovative eye-tracking experiment in a six degrees of freedom (6-DOF) VR environment.<n>Our proposed model predicts saliency maps for textured mesh surfaces by treating each triangular face as an individual unit and assigning a saliency density value to reflect the importance of each local surface region.
  arXiv  Detail & Related papers  (2024-12-11T08:27:33Z)
• OpenMaterial: A Large-scale Dataset of Complex Materials for 3D Reconstruction [55.052637670485716]
  We introduce OpenMaterial, a large-scale semi-synthetic dataset for material-aware 3D reconstruction.<n>It comprises 1,001 objects spanning 295 distinct materials, including conductors, dielectrics, plastics, and their roughened variants, captured under 714 diverse lighting conditions.<n>It provides multi-view images, 3D shape models, camera poses, depth maps, and object masks, establishing the first extensive benchmark for evaluating 3D reconstruction on challenging materials.
  arXiv  Detail & Related papers  (2024-06-13T07:46:17Z)
• 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)
• Multi-View Neural Surface Reconstruction with Structured Light [7.709526244898887]
  Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. We introduce active sensing with structured light (SL) into multi-view 3D object reconstruction based on DR to learn the unknown geometry and appearance of arbitrary scenes and camera poses. Our method realizes high reconstruction accuracy in the textureless region and reduces efforts for camera pose calibration.
  arXiv  Detail & Related papers  (2022-11-22T03:10:46Z)
• Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes [75.63464905190061]
  Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this problem. We propose a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera. Our experiments show successful recovery of high-quality 3D geometry for complex transparent shapes using as few as 5-12 natural images.
  arXiv  Detail & Related papers  (2020-04-22T23:51:30Z)

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.