3D Neural Edge Reconstruction

• URL: http://arxiv.org/abs/2405.19295v1
• Date: Wed, 29 May 2024 17:23:51 GMT
• Title: 3D Neural Edge Reconstruction
• Authors: Lei Li, Songyou Peng, Zehao Yu, Shaohui Liu, Rémi Pautrat, Xiaochuan Yin, Marc Pollefeys,
• Abstract summary: We introduce EMAP, a new method for learning 3D edge representations with a focus on both lines and curves. Our method implicitly encodes 3D edge distance and direction in Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this neural representation, we propose an edge extraction algorithm that robustly abstracts 3D edges from the inferred edge points and their directions.
• Score: 61.10201396044153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world objects and environments are predominantly composed of edge features, including straight lines and curves. Such edges are crucial elements for various applications, such as CAD modeling, surface meshing, lane mapping, etc. However, existing traditional methods only prioritize lines over curves for simplicity in geometric modeling. To this end, we introduce EMAP, a new method for learning 3D edge representations with a focus on both lines and curves. Our method implicitly encodes 3D edge distance and direction in Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this neural representation, we propose an edge extraction algorithm that robustly abstracts parametric 3D edges from the inferred edge points and their directions. Comprehensive evaluations demonstrate that our method achieves better 3D edge reconstruction on multiple challenging datasets. We further show that our learned UDF field enhances neural surface reconstruction by capturing more details.

Related papers

• EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting [33.43750488033706]
  State-of-the-art image-based methods learn a 3D edge point cloud then fit 3D edges to it. Our method learns explicitly the 3D edge points and their edge direction hence bypassing the need for point sampling. Results show that the proposed method produces edges as accurate and complete as the state-of-the-art while being an order of magnitude faster.
  arXiv  Detail & Related papers  (2024-09-19T16:28:45Z)
• ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds [52.03819676074455]
  ParaPoint is an unsupervised neural learning pipeline for achieving global free-boundary surface parameterization. This work makes the first attempt to investigate neural point cloud parameterization that pursues both global mappings and free boundaries.
  arXiv  Detail & Related papers  (2024-03-15T14:35:05Z)
• SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes [16.355677959323426]
  We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling.
  arXiv  Detail & Related papers  (2023-04-13T13:37:21Z)
• NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images [18.303674194874457]
  We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images. We learn a neural implicit field representing the density distribution of 3D edges which we refer to as Neural Edge Field (NEF) NEF is optimized with a view-based rendering loss where a 2D edge map is rendered at a given view and is compared to the ground-truth edge map extracted from the image of that view.
  arXiv  Detail & Related papers  (2023-03-14T06:45:13Z)
• GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation [73.77505964222632]
  We present a learning-based method, namely GeoUDF, to tackle the problem of reconstructing a discrete surface from a sparse point cloud. To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation. To extract triangle meshes from the predicted UDF, we propose a customized edge-based marching cube module.
  arXiv  Detail & Related papers  (2022-11-30T06:02:01Z)
• GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs [49.55919802779889]
  We propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. In this work, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning. Our method achieves the state-of-the-art performance, especially when compared in the case of using only a few propagation steps.
  arXiv  Detail & Related papers  (2022-10-19T17:56:03Z)
• MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation [91.6658845016214]
  We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. We render a 3D shape from multiple views, and set up a dense correspondence learning task within the contrastive learning framework. As a result, the learned 2D representations are view-invariant and geometrically consistent.
  arXiv  Detail & Related papers  (2022-08-18T00:48:15Z)
• Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion [53.885984328273686]
  Implicit Feature Networks (IF-Nets) deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data. IF-Nets clearly outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions.
  arXiv  Detail & Related papers  (2020-03-03T11:14:29Z)
• 3D Shape Segmentation with Geometric Deep Learning [2.512827436728378]
  We propose a neural-network based approach that produces 3D augmented views of the 3D shape to solve the whole segmentation as sub-segmentation problems. We validate our approach using 3D shapes of publicly available datasets and of real objects that are reconstructed using photogrammetry techniques.
  arXiv  Detail & Related papers  (2020-02-02T14:11:16Z)

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.