PlueckerNet: Learn to Register 3D Line Reconstructions

• URL: http://arxiv.org/abs/2012.01096v1
• Date: Wed, 2 Dec 2020 11:31:56 GMT
• Title: PlueckerNet: Learn to Register 3D Line Reconstructions
• Authors: Liu Liu, Hongdong Li, Haodong Yao and Ruyi Zha
• Abstract summary: 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.
• Score: 57.20244406275875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aligning two partially-overlapped 3D line reconstructions in Euclidean space is challenging, as we need to simultaneously solve correspondences and relative pose between line reconstructions. This paper proposes a neural network based method and it has three modules connected in sequence: (i) a Multilayer Perceptron (MLP) based network takes Pluecker representations of lines as inputs, to extract discriminative line-wise features and matchabilities (how likely each line is going to have a match), (ii) an Optimal Transport (OT) layer takes two-view line-wise features and matchabilities as inputs to estimate a 2D joint probability matrix, with each item describes the matchness of a line pair, and (iii) line pairs with Top-K matching probabilities are fed to a 2-line minimal solver in a RANSAC framework to estimate a six Degree-of-Freedom (6-DoF) rigid transformation. Experiments on both indoor and outdoor datasets show that the registration (rotation and translation) precision of our method outperforms baselines significantly.

Related papers

• Semantic Line Combination Detector [17.60109693530759]
  A novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets.
  arXiv  Detail & Related papers  (2024-04-29T03:21:05Z)
• 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)
• IDLS: Inverse Depth Line based Visual-Inertial SLAM [9.38589798999922]
  Inverse Depth Line SLAM (IDLS) is proposed to track the line features in SLAM in an accurate and efficient way. IDLS is extensively evaluated in multiple perceptually-challenging datasets.
  arXiv  Detail & Related papers  (2023-04-23T20:53:05Z)
• Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning [112.54086514317021]
  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.
  arXiv  Detail & Related papers  (2022-10-24T06:39:32Z)
• DFC: Deep Feature Consistency for Robust Point Cloud Registration [0.4724825031148411]
  We present a novel learning-based alignment network for complex alignment scenes. We validate our approach on the 3DMatch dataset and the KITTI odometry dataset.
  arXiv  Detail & Related papers  (2021-11-15T08:27:21Z)
• Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization [59.87663954467815]
  Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer. In this paper, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we unroll a projection-free algorithm for semi-definite programming relaxation (SDR) of a binary graph. Experimental results show that our unrolled network outperformed pure model-based graph classifiers, and achieved comparable performance to pure data-driven networks but using far fewer parameters.
  arXiv  Detail & Related papers  (2021-09-10T07:01:15Z)
• Subdivision-Based Mesh Convolution Networks [38.09613983540932]
  Convolutional neural networks (CNNs) have made great breakthroughs in 2D computer vision. This paper introduces a novel CNN framework, named SubdivNet, for 3D triangle meshes with Loop subdivision sequence connectivity. Experiments on mesh classification, segmentation, correspondence, and retrieval from the real-world demonstrate the effectiveness and efficiency of SubdivNet.
  arXiv  Detail & Related papers  (2021-06-04T06:50:34Z)
• Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem [98.92148855291363]
  This paper proposes a deep CNN model which simultaneously solves for both 6-DoF absolute camera pose 2D--3D correspondences. Tests on both real and simulated data have shown that our method substantially outperforms existing approaches.
  arXiv  Detail & Related papers  (2020-03-15T04:17:30Z)
• 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.