EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation

• URL: http://arxiv.org/abs/2103.02517v1
• Date: Wed, 3 Mar 2021 16:43:08 GMT
• Title: EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation
• Authors: Yecheng Lyu, Xinming Huang, Ziming Zhang
• Abstract summary: Point cloud representation in 2D space has attracted increasing research interest since it exposes the local geometry features in a 2D space. We propose a novel 2D representation method that projects a point cloud onto an ellipsoid surface space, where local patterns are well exposed in ellipsoid-level and point-level. A novel convolutional neural network named EllipsoidNet is proposed to utilize those features for point cloud classification and segmentation applications.
• Score: 24.36469430784483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud patterns are hard to learn because of the implicit local geometry features among the orderless points. In recent years, point cloud representation in 2D space has attracted increasing research interest since it exposes the local geometry features in a 2D space. By projecting those points to a 2D feature map, the relationship between points is inherited in the context between pixels, which are further extracted by a 2D convolutional neural network. However, existing 2D representing methods are either accuracy limited or time-consuming. In this paper, we propose a novel 2D representation method that projects a point cloud onto an ellipsoid surface space, where local patterns are well exposed in ellipsoid-level and point-level. Additionally, a novel convolutional neural network named EllipsoidNet is proposed to utilize those features for point cloud classification and segmentation applications. The proposed methods are evaluated in ModelNet40 and ShapeNet benchmarks, where the advantages are clearly shown over existing 2D representation methods.

Related papers

• Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding [11.416392706435415]
  Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs) Existing strategies directly map Vision-Language Models from 2D pixels of rendered or captured views to 3D points, overlooking the inherent and expressible point cloud geometric structure. We introduce the first training-free aggregation technique that leverages the point cloud's 3D geometric structure to improve the quality of the transferred Vision-Language Models.
  arXiv  Detail & Related papers  (2023-12-04T12:30:07Z)
• Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation [62.258256483231484]
  LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Many works project the point cloud onto the 2D image and adopt the 2D Convolutional Neural Networks (CNNs) or vision transformer for LiDAR point cloud semantic segmentation. In this paper, we propose a novel spherical frustum structure to avoid quantized information loss.
  arXiv  Detail & Related papers  (2023-11-29T09:55:13Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [122.53774221136193]
  State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution. A natural remedy is to utilize the 3D voxelization and 3D convolution network. We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
  arXiv  Detail & Related papers  (2021-09-12T06:25:11Z)
• Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud [50.56461318879761]
  We propose Geometry-Disentangled Attention Network (GDANet) for 3D image processing. GDANet disentangles point clouds into contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components. Experiments on 3D object classification and segmentation benchmarks demonstrate that GDANet achieves the state-of-the-arts with fewer parameters.
  arXiv  Detail & Related papers  (2020-12-20T13:35:00Z)
• ODFNet: Using orientation distribution functions to characterize 3D point clouds [0.0]
  We leverage on point orientation distributions around a point in order to obtain an expressive local neighborhood representation for point clouds. New ODFNet model achieves state-of-the-art accuracy for object classification on ModelNet40 and ScanObjectNN datasets.
  arXiv  Detail & Related papers  (2020-12-08T19:54:20Z)
• ParaNet: Deep Regular Representation for 3D Point Clouds [62.81379889095186]
  ParaNet is a novel end-to-end deep learning framework for representing 3D point clouds. It converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI) In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible.
  arXiv  Detail & Related papers  (2020-12-05T13:19:55Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)

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.