GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation

• URL: http://arxiv.org/abs/2207.11484v1
• Date: Sat, 23 Jul 2022 10:29:26 GMT
• Title: GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation
• Authors: Keqiang Li, Mingyang Zhao, Huaiyu Wu, Dong-Ming Yan, Zhen Shen, Fei-Yue Wang and Gang Xiong
• Abstract summary: We propose a precise and efficient normal estimation method for unstructured 3D point clouds. We learn graph convolutional feature representation for normal estimation, which emphasizes more local neighborhood geometry. Our method outperforms competitors with the state-of-the-art accuracy on various benchmark datasets.
• Score: 31.40738037512243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds. Unlike existing approaches that directly take patches and ignore the local neighborhood relationships, which make them susceptible to challenging regions such as sharp edges, we propose to learn graph convolutional feature representation for normal estimation, which emphasizes more local neighborhood geometry and effectively encodes intrinsic relationships. Additionally, we design a novel adaptive module based on the attention mechanism to integrate point features with their neighboring features, hence further enhancing the robustness of the proposed normal estimator against point density variations. To make it more distinguishable, we introduce a multi-scale architecture in the graph block to learn richer geometric features. Our method outperforms competitors with the state-of-the-art accuracy on various benchmark datasets, and is quite robust against noise, outliers, as well as the density variations.

Related papers

• Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence [92.07601770031236]
  We investigate semantically meaningful patterns in the attention heads of an encoder-only Transformer architecture. We find that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization.
  arXiv  Detail & Related papers  (2024-09-20T07:41:47Z)
• Geometry-aware Feature Matching for Large-Scale Structure from Motion [10.645087195983201]
  We introduce geometry cues in addition to color cues to fill gaps when there is less overlap in large-scale scenarios. Our approach ensures that the denser correspondences from detector-free methods are geometrically consistent and more accurate. It outperforms state-of-the-art feature matching methods on benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-03T21:41:35Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• Joint Dense-Point Representation for Contour-Aware Graph Segmentation [2.138299283227551]
  We present a novel methodology that combines graph and dense segmentation techniques by jointly learning both point and pixel contour representations. This addresses deficiencies in typical graph segmentation methods where misaligned objectives restrict the network from learning discriminative and contour features. Our approach is validated on several Chest X-ray datasets, demonstrating clear improvements in segmentation stability and accuracy.
  arXiv  Detail & Related papers  (2023-06-21T10:07:17Z)
• Data-heterogeneity-aware Mixing for Decentralized Learning [63.83913592085953]
  We characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. Motivated by our analysis, we propose an approach that periodically and efficiently optimize the metric.
  arXiv  Detail & Related papers  (2022-04-13T15:54:35Z)
• PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows [58.96306192736593]
  We present PU-Flow, which incorporates normalizing flows and feature techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we formulate the upsampling process as point in a latent space, where the weights are adaptively learned from local geometric context. We show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.
  arXiv  Detail & Related papers  (2021-07-13T07:45:48Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• Learning non-Gaussian graphical models via Hessian scores and triangular transport [6.308539010172309]
  We propose an algorithm for learning the Markov structure of continuous and non-Gaussian distributions. Our algorithm SING estimates the density using a deterministic coupling, induced by a triangular transport map, and iteratively exploits sparse structure in the map to reveal sparsity in the graph.
  arXiv  Detail & Related papers  (2021-01-08T16:42:42Z)
• MG-SAGC: A multiscale graph and its self-adaptive graph convolution network for 3D point clouds [6.504546503077047]
  We propose a multiscale graph generation method for point clouds. This approach transforms point clouds into a structured multiscale graph form that supports multiscale analysis of point clouds in the scale space. Because traditional convolutional neural networks are not applicable to graph data with irregular neighborhoods, this paper presents an sef-adaptive convolution kernel that uses the Chebyshev graph to fit an irregular convolution filter.
  arXiv  Detail & Related papers  (2020-12-23T01:58:41Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)
• Learning Graph-Convolutional Representations for Point Cloud Denoising [31.557988478764997]
  We propose a deep neural network that can deal with the permutation-invariance problem encountered by learning-based point cloud processing methods. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs. It is especially robust both at high noise levels and in presence of structured noise such as the one encountered in real LiDAR scans.
  arXiv  Detail & Related papers  (2020-07-06T08:11:28Z)

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.