High-fidelity Multi-view Normal Integration with Scale-encoded Neural Surface Representation

• URL: http://arxiv.org/abs/2603.20337v1
• Date: Fri, 20 Mar 2026 08:58:10 GMT
• Title: High-fidelity Multi-view Normal Integration with Scale-encoded Neural Surface Representation
• Abstract summary: We propose a scale-encoded neural surface representation that incorporates the pixel coverage area into the neural representation.<n>By associating each 3D point with a spatial scale and calculating its normal from a hybrid grid-based encoding, our method effectively represents multi-scale surface normals captured at varying distances.
• Score: 33.628831556211225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous multi-view normal integration methods typically sample a single ray per pixel, without considering the spatial area covered by each pixel, which varies with camera intrinsics and the camera-to-object distance. Consequently, when the target object is captured at different distances, the normals at corresponding pixels may differ across views. This multi-view surface normal inconsistency results in the blurring of high-frequency details in the reconstructed surface. To address this issue, we propose a scale-encoded neural surface representation that incorporates the pixel coverage area into the neural representation. By associating each 3D point with a spatial scale and calculating its normal from a hybrid grid-based encoding, our method effectively represents multi-scale surface normals captured at varying distances. Furthermore, to enable scale-aware surface reconstruction, we introduce a mesh extraction module that assigns an optimal local scale to each vertex based on the training observations. Experimental results demonstrate that our approach consistently yields high-fidelity surface reconstruction from normals observed at varying distances, outperforming existing multi-view normal integration methods.

Related papers

• Spatially Coupled Phase-to-Depth Calibration for Fringe Projection Profilometry [0.0]
  In projection profilometry, depth is commonly recovered by fitting a phase-to-depth relation independently at each camera pixel.<n>We propose a spatially coupled phase-depth transformation in which all pixels share a single low-dimensional mapping-global phase scalars.<n>On a dental target with high-resolution scanner ground truth, the proposed model attains point-to-surface RMSE comparable to an active-stereo reference.
  arXiv  Detail & Related papers  (2026-06-10T02:58:37Z)
• IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation [76.36174247570716]
  We propose IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images.<n> IVGT learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions.<n>It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints.
  arXiv  Detail & Related papers  (2026-05-15T17:59:57Z)
• WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting [51.69408870574092]
  We present WorldMirror, an all-in-one, feed-forward model for versatile 3D geometric prediction tasks.<n>Our framework flexibly integrates diverse geometric priors, including camera poses, intrinsics, and depth maps.<n>WorldMirror achieves state-of-the-art performance across diverse benchmarks from camera, point map, depth, and surface normal estimation to novel view synthesis.
  arXiv  Detail & Related papers  (2025-10-12T17:59:09Z)
• Multi-view Normal and Distance Guidance Gaussian Splatting for Surface Reconstruction [2.760653393100493]
  3D Gaussian Splatting (3DGS) achieves remarkable results in the field of surface reconstruction.<n>However, when Gaussian normal vectors are aligned within the single-view projection plane, while the geometry appears reasonable in the current view, biases may emerge upon switching to nearby views.<n>We develop a multi-view normal enhancement module, which ensures consistency across views by matching the normals of pixel points in nearby views and calculating the loss.
  arXiv  Detail & Related papers  (2025-08-11T07:25:13Z)
• Feature-Preserving Mesh Decimation for Normal Integration [16.320467417627277]
  Normal integration reconstructs 3D surfaces from normal maps obtained by photometric stereo.<n>In this work, we replace the dense pixel grid with a sparse anisotropic triangle mesh prior to normal integration.
  arXiv  Detail & Related papers  (2025-04-01T14:56:47Z)
• An Adaptive Screen-Space Meshing Approach for Normal Integration [0.0]
  This work introduces an adaptive surface triangulation in the image domain and afterwards performs the normal integration on a triangle mesh. Based on the curvature, we identify flat areas and aggregate pixels into triangles. Compared to pixel grids, our triangle meshes adapt locally to surface details and allow for a sparser representation.
  arXiv  Detail & Related papers  (2024-09-25T13:12:58Z)
• Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement [50.56517624931987]
  The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions. Recent methods leverage neural radiance fields aided by predicted surface normal priors to recover the scene geometry. This work aims to reconstruct high-fidelity surfaces with fine-grained details by addressing the above limitations.
  arXiv  Detail & Related papers  (2023-09-14T12:05:29Z)
• Multi-View Stereo Representation Revisit: Region-Aware MVSNet [8.264851594332677]
  Deep learning-based multi-view stereo has emerged as a powerful paradigm for reconstructing the complete geometrically-detailed objects from multi-views. We propose RA-MVSNet to take advantage of point-to-surface distance so that the model is able to perceive a wider range of surfaces. Our proposed RA-MVSNet is patch-awared, since the perception range is enhanced by associating hypothetical planes with a patch of surface.
  arXiv  Detail & Related papers  (2023-04-26T15:17:51Z)
• HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces [54.77683371400133]
  We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Experimental results show that our HSurf-Net achieves the state-of-the-art performance on the synthetic shape dataset.
  arXiv  Detail & Related papers  (2022-10-13T16:39:53Z)
• Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
  We present an efficient method for joint optimization of materials and lighting from multi-view image observations. We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
  arXiv  Detail & Related papers  (2021-11-24T13:58:20Z)
• Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
  We present a modern solution to the multi-view photometric stereo problem (MVPS) We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
  arXiv  Detail & Related papers  (2021-10-11T20:20:03Z)
• 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.