UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections

• URL: http://arxiv.org/abs/2312.13285v2
• Date: Mon, 09 Dec 2024 20:19:58 GMT
• Title: UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections
• Authors: Fangjinhua Wang, Marie-Julie Rakotosaona, Michael Niemeyer, Richard Szeliski, Marc Pollefeys, Federico Tombari,
• Abstract summary: We propose UniSDF, a general purpose 3D reconstruction method that can reconstruct large complex scenes with reflections. Our method is able to robustly reconstruct complex large-scale scenes with fine details and reflective surfaces, leading to the best overall performance.
• Score: 87.191742674543
• License:
• Abstract: Neural 3D scene representations have shown great potential for 3D reconstruction from 2D images. However, reconstructing real-world captures of complex scenes still remains a challenge. Existing generic 3D reconstruction methods often struggle to represent fine geometric details and do not adequately model reflective surfaces of large-scale scenes. Techniques that explicitly focus on reflective surfaces can model complex and detailed reflections by exploiting better reflection parameterizations. However, we observe that these methods are often not robust in real scenarios where non-reflective as well as reflective components are present. In this work, we propose UniSDF, a general purpose 3D reconstruction method that can reconstruct large complex scenes with reflections. We investigate both camera view as well as reflected view-based color parameterization techniques and find that explicitly blending these representations in 3D space enables reconstruction of surfaces that are more geometrically accurate, especially for reflective surfaces. We further combine this representation with a multi-resolution grid backbone that is trained in a coarse-to-fine manner, enabling faster reconstructions than prior methods. Extensive experiments on object-level datasets DTU, Shiny Blender as well as unbounded datasets Mip-NeRF 360 and Ref-NeRF real demonstrate that our method is able to robustly reconstruct complex large-scale scenes with fine details and reflective surfaces, leading to the best overall performance. Project page: \url{https://fangjinhuawang.github.io/UniSDF}.

Related papers

• 3D Face Reconstruction With Geometry Details From a Single Color Image Under Occluded Scenes [4.542616945567623]
  3D face reconstruction technology aims to generate a face stereo model naturally and realistically. Previous deep face reconstruction approaches are typically designed to generate convincing textures. By introducing bump mapping, we successfully added mid-level details to coarse 3D faces.
  arXiv  Detail & Related papers  (2024-12-25T15:16:02Z)
• AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction [55.69271635843385]
  We present AniSDF, a novel approach that learns fused-granularity neural surfaces with physics-based encoding for high-fidelity 3D reconstruction. Our method boosts the quality of SDF-based methods by a great scale in both geometry reconstruction and novel-view synthesis.
  arXiv  Detail & Related papers  (2024-10-02T03:10:38Z)
• NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images [62.752710734332894]
  NeRSP is a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. We derive photometric and geometric cues from the polarimetric image formation model and multiview azimuth consistency. We achieve the state-of-the-art surface reconstruction results with only 6 views as input.
  arXiv  Detail & Related papers  (2024-06-11T09:53:18Z)
• NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images [44.1333444097976]
  We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment.
  arXiv  Detail & Related papers  (2023-05-27T07:40:07Z)
• gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction [94.46581592405066]
  We exploit the hand structure and use it as guidance for SDF-based shape reconstruction. We predict kinematic chains of pose transformations and align SDFs with highly-articulated hand poses.
  arXiv  Detail & Related papers  (2023-04-24T10:05:48Z)
• A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images [15.40230841242637]
  We present a novel hierarchical representation network (HRN) to achieve accurate and detailed face reconstruction from a single image. Our framework can be extended to a multi-view fashion by considering detail consistency of different views. Our method outperforms the existing methods in both reconstruction accuracy and visual effects.
  arXiv  Detail & Related papers  (2023-02-28T09:24:36Z)
• VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction [64.09702079593372]
  VolRecon is a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF) On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction.
  arXiv  Detail & Related papers  (2022-12-15T18:59:54Z)
• 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)
• Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
  We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object. We first estimate per-view depth maps using a deep multi-view stereo network. These depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture.
  arXiv  Detail & Related papers  (2020-03-27T21:28:54Z)

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.