Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

• URL: http://arxiv.org/abs/2210.12971v2
• Date: Wed, 6 Sep 2023 03:06:11 GMT
• Title: Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning
• Authors: Nan Xue, Tianfu Wu, Song Bai, Fu-Dong Wang, Gui-Song Xia, Liangpei Zhang, Philip H.S. Torr
• Abstract summary: This article presents HolisticDally-Attracted Wireframe Parsing 2 method for geometric analysis using line segments and junctions. The proposed HAWP consists of three components empowered by end-to-form 4D labels.
• Score: 112.54086514317021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: (1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, (2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and (3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.

Related papers

• PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction [72.75478398447396]
  We propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane.
  arXiv  Detail & Related papers  (2023-08-31T17:57:17Z)
• NEAT: Distilling 3D Wireframes from Neural Attraction Fields [52.90572335390092]
  This paper studies the problem of structured lineframe junctions using 3D reconstruction segments andFocusing junctions. ProjectNEAT enjoys the joint neural fields and view without crossart matching from scratch.
  arXiv  Detail & Related papers  (2023-07-14T07:25:47Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• PlueckerNet: Learn to Register 3D Line Reconstructions [57.20244406275875]
  This paper proposes a neural network based method to solve the problem of Aligning two partially-overlapped 3D line reconstructions in Euclidean space. Experiments on both indoor and outdoor datasets show that the registration (rotation and translation) precision of our method outperforms baselines significantly.
  arXiv  Detail & Related papers  (2020-12-02T11:31:56Z)
• Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds [2.924868086534434]
  This paper introduces a novel approach for 3D point cloud semantic segmentation that exploits multiple projections of the point cloud. Our Multi-Projection Fusion framework analyzes spherical and bird's-eye view projections using two separate highly-efficient 2D fully convolutional models.
  arXiv  Detail & Related papers  (2020-11-03T19:40:43Z)
• Learning to Parse Wireframes in Images of Man-Made Environments [40.471924713855735]
  We propose a "wireframe"-based approach to the task of representation for manmade environments. We have built networks that are suitable for extracting two large lines and their junctions.
  arXiv  Detail & Related papers  (2020-07-15T07:54:18Z)
• Holistically-Attracted Wireframe Parsing [123.58263152571952]
  This paper presents a fast and parsimonious parsing method to detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification.
  arXiv  Detail & Related papers  (2020-03-03T17:43:57Z)

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.