Shape-Oriented Convolution Neural Network for Point Cloud Analysis
Point cloud is a principal data structure adopted for 3D geometric information encoding. Unlike other conventional visual data, such as images and videos, these irregular points describe the complex shape features of 3D objects, which makes shape feature learning an essential component of point cloud analysis. To this end, a shape-oriented message passing scheme dubbed ShapeConv is proposed to focus on the representation learning of the underlying shape formed by each local neighboring point. Despite this intra-shape relationship learning, ShapeConv is also designed to incorporate the contextual effects from the inter-shape relationship through capturing the long-ranged dependencies between local underlying shapes. This shape-oriented operator is stacked into our hierarchical learning architecture, namely Shape-Oriented Convolutional Neural Network (SOCNN), developed for point cloud analysis. Extensive experiments have been performed to evaluate its significance in the tasks of point cloud classification and part segmentation.
Code (0)
등록된 구현이 없습니다.
Tasks
Point Cloud ClassificationRepresentation LearningSimilar Papers 제목 키워드 기반
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular gri…
3D Part Segmentation3D Point Cloud ClassificationPoint Cloud ClassificationRelationDynamic Plane Convolutional Occupancy Networks
Learning-based 3D reconstruction using implicit neural representations has shown promising progress not only at the object level but also in more complicated scenes. In this paper, we propose Dynamic Plane Convolutional …
3D ReconstructionSurface ReconstructionPoint2Mesh: A Self-Prior for Deformable Meshes
In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape properties, the prior is defined a…
CloudWalker: Random walks for 3D point cloud shape analysis
Point clouds are gaining prominence as a method for representing 3D shapes, but their irregular structure poses a challenge for deep learning methods. In this paper we propose CloudWalker, a novel method for learning 3D …
RetrievalConcavity-Induced Distance for Unoriented Point Cloud Decomposition
We propose Concavity-induced Distance (CID) as a novel way to measure the dissimilarity between a pair of points in an unoriented point cloud. CID indicates the likelihood of two points or two sets of points belonging to…
Instance SegmentationSemantic Segmentation