CLEAR-NeRF: Collinearity and Local-region Enhanced Accurate 3D Reconstruction in Unbounded Scenes

• URL: http://arxiv.org/abs/2605.28125v1
• Date: Wed, 27 May 2026 08:16:48 GMT
• Title: CLEAR-NeRF: Collinearity and Local-region Enhanced Accurate 3D Reconstruction in Unbounded Scenes
• Abstract summary: This study adapts NeRF-based 3D reconstruction to multi-region of interest unbounded scenes to improve robustness to lighting and pose variation while enforcing metric accuracy suitable for digital-twin applications.<n>Results indicate superior performance of the proposed pipeline over the baseline NeRF models and established Structure from Motion (SfM) - Multi-View Stereo (MVS) solutions.
• Score: 1.150550961323334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world 3D reconstruction applications demand photorealism and metric accuracy across unbounded, complex scenes with challenging lighting and imperfect captures that current Neural Radiance Field (NeRF) pipelines only partly satisfy. This study adapts NeRF-based 3D reconstruction to multi-region of interest unbounded scenes to improve robustness to lighting and pose variation while enforcing metric accuracy suitable for digital-twin applications. Our approach introduces (i) automated local region localization/detection and reconstruction to seamlessly prioritize areas of interest without proliferating submodules, (ii) collinearity-enforcing ray sampling to learn smooth planar and curved surfaces, (iii) depth-localized neighborhood point extraction to suppress surface artifacts, and (iv) geometry-relevant color aggregation to mitigate lighting- and pose-caused variations. Results indicate superior performance of the proposed pipeline over the baseline NeRF models and established Structure from Motion (SfM) - Multi-View Stereo (MVS) solutions.

Related papers

• Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals [7.144634906657132]
  Reconstructing object geometry from radio frequency (RF) signals is fundamentally challenging due to the lensless imaging nature of RF signals.<n>In this paper, we introduce a Unified LoS and NLoS neural geometry reconstruction framework GeRaF 2.0.<n>By integrating visual LoS priors into the neural field formulation, GeRaF 2.0 achieves stable training and physically consistent reconstruction of both visible and hidden geometry.
  arXiv  Detail & Related papers  (2026-05-27T20:58:41Z)
• PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction [76.9760302941819]
  PolGS++ is a polarimetric Gaussian Splatting framework for fast reflective surface reconstruction.<n>We introduce a depth-guided visibility mask acquisition mechanism that enables angle-of-polarization (AoP)-based tangent-space consistency constraints.
  arXiv  Detail & Related papers  (2026-03-11T14:11:42Z)
• L3DR: 3D-aware LiDAR Diffusion and Rectification [85.5914944339043]
  Range-view (RV) based LiDAR diffusion has recently made huge strides towards 2D photo-realism.<n>However, it neglects 3D geometry realism and often generates various RV artifacts such as depth bleeding and wavy surfaces.<n>We design L3DR, a 3D-aware LiDAR Diffusion and Rectification framework that can regress and cancel RV artifacts in 3D space.
  arXiv  Detail & Related papers  (2026-02-22T06:31:58Z)
• 3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction [62.84879632157956]
  We propose a novel hybrid 2D/3D representation that jointly optimize constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene.<n>Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric accuracy.<n>It achieves state-of-the-art depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to a specific camera model.
  arXiv  Detail & Related papers  (2025-09-19T21:04:36Z)
• HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity [8.74691272469226]
  HiNeuS is a unified framework that holistically addresses three core limitations in existing approaches.<n>We introduce: 1) Differential visibility verification through SDF-guided ray tracing; 2) Planar-conformal regularization via ray-aligned geometry patches; and 3) Physically-grounded Eikonal relaxation that dynamically modulates geometric constraints based on local gradients.
  arXiv  Detail & Related papers  (2025-06-30T13:45:25Z)
• RaNeuS: Ray-adaptive Neural Surface Reconstruction [87.20343320266215]
  We leverage a differentiable radiance field eg NeRF to reconstruct detailed 3D surfaces in addition to producing novel view renderings. Considering that different methods formulate and optimize the projection from SDF to radiance field with a globally constant Eikonal regularization, we improve with a ray-wise weighting factor. Our proposed textitRaNeuS are extensively evaluated on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2024-06-14T07:54:25Z)
• NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising [23.876281686625134]
  We present NeSLAM, a framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis.
  arXiv  Detail & Related papers  (2024-03-29T07:59:37Z)
• Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation [51.346733271166926]
  Mesh2NeRF is an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. We validate the effectiveness of Mesh2NeRF across various tasks.
  arXiv  Detail & Related papers  (2024-03-28T11:22:53Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [73.50359502037232]
  VoxNeRF is a novel approach to enhance the quality and efficiency of neural indoor reconstruction and novel view synthesis.<n>We propose an efficient voxel-guided sampling technique that allocates computational resources to selectively the most relevant segments of rays.<n>Our approach is validated with extensive experiments on ScanNet and ScanNet++.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• HarmonicNeRF: Geometry-Informed Synthetic View Augmentation for 3D Scene Reconstruction in Driving Scenarios [2.949710700293865]
  HarmonicNeRF is a novel approach for outdoor self-supervised monocular scene reconstruction. It capitalizes on the strengths of NeRF and enhances surface reconstruction accuracy by augmenting the input space with geometry-informed synthetic views. Our approach establishes new benchmarks in synthesizing novel depth views and reconstructing scenes, significantly outperforming existing methods.
  arXiv  Detail & Related papers  (2023-10-09T07:42:33Z)

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.