Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization
- URL: http://arxiv.org/abs/2412.20328v1
- Date: Sun, 29 Dec 2024 02:54:01 GMT
- Title: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization
- Authors: Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang,
- Abstract summary: Multi-view stereo (MVS) reconstruction of low-textured areas is a prominent research focus.
Traditional MVS methods often encounter issues such as crossing object boundaries and limited perception ranges.
We introduce dual-level precision edge information, including fine and coarse edges, to enhance the robustness of plane model construction.
Our method achieves state-of-the-art performance on the ETH3D and Tanks & Temples benchmarks.
- Score: 3.597821311597427
- License:
- Abstract: The reconstruction of low-textured areas is a prominent research focus in multi-view stereo (MVS). In recent years, traditional MVS methods have performed exceptionally well in reconstructing low-textured areas by constructing plane models. However, these methods often encounter issues such as crossing object boundaries and limited perception ranges, which undermine the robustness of plane model construction. Building on previous work (APD-MVS), we propose the DPE-MVS method. By introducing dual-level precision edge information, including fine and coarse edges, we enhance the robustness of plane model construction, thereby improving reconstruction accuracy in low-textured areas. Furthermore, by leveraging edge information, we refine the sampling strategy in conventional PatchMatch MVS and propose an adaptive patch size adjustment approach to optimize matching cost calculation in both stochastic and low-textured areas. This additional use of edge information allows for more precise and robust matching. Our method achieves state-of-the-art performance on the ETH3D and Tanks & Temples benchmarks. Notably, our method outperforms all published methods on the ETH3D benchmark.
Related papers
- CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction [5.528874948395173]
3D Gaussian Splatting (3DGS) has emerged as a prominent method for scene representation and reconstruction.
We propose a novel cross-view Gaussian Splatting method for large-scale scene reconstruction, based on dual-branch fusion.
Our method achieves superior performance in novel view synthesis compared to state-of-the-art methods.
arXiv Detail & Related papers (2025-01-03T08:24:59Z) - Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders [87.17440422575721]
We present Dora-VAE, a novel approach that enhances VAE reconstruction through our proposed sharp edge sampling strategy and a dual cross-attention mechanism.
To systematically evaluate VAE reconstruction quality, we additionally propose Dora-bench, a benchmark that quantifies shape complexity through the density of sharp edges.
arXiv Detail & Related papers (2024-12-23T18:59:06Z) - GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene.
Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
arXiv Detail & Related papers (2024-11-29T03:54:54Z) - Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans [1.0436971860292366]
We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures.
The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets.
On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes.
arXiv Detail & Related papers (2024-08-02T18:36:45Z) - SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene.
SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
arXiv Detail & Related papers (2024-04-29T23:26:30Z) - SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical
Refinement and EM optimization [6.886220026399106]
We introduce Multi-View Stereo (SD-MVS) to tackle challenges in 3D reconstruction of textureless areas.
We are the first to adopt the Segment Anything Model (SAM) to distinguish semantic instances in scenes.
We propose a unique refinement strategy that combines spherical coordinates and gradient descent on normals and pixelwise search interval on depths.
arXiv Detail & Related papers (2024-01-12T05:25:57Z) - 360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception [56.84921040837699]
Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results.
We propose an orthogonal plane disentanglement network (termed DOPNet) to distinguish ambiguous semantics.
We also present an unsupervised adaptation technique tailored for horizon-depth and ratio representations.
Our solution outperforms other SoTA models on both monocular layout estimation and multi-view layout estimation tasks.
arXiv Detail & Related papers (2023-12-26T12:16:03Z) - NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion [56.98287481620215]
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured.
Our approach builds on two recent developments: surface reconstruction using neural radiance fields for the reconstruction of the visible parts of the surface, and guidance of pre-trained 2D diffusion models in the form of Score Distillation Sampling (SDS) to complete the shape in unobserved regions in a plausible manner.
arXiv Detail & Related papers (2023-12-07T19:30:55Z) - MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View
Stereo [7.130834755320434]
We propose a resilient and effective multi-view stereo approach (MP-MVS)
We design a multi-scale windows PatchMatch (mPM) to obtain reliable depth of untextured areas.
In contrast with other multi-scale approaches, which is faster and can be easily extended to PatchMatch-based MVS approaches.
arXiv Detail & Related papers (2023-09-23T07:30:42Z) - MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based
Self-Supervised Pre-Training [58.07391711548269]
Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training.
Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training.
arXiv Detail & Related papers (2023-03-23T17:59:02Z)
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.