Papers 3D Object Classification
“3D Object Classification” 태그가 달린 논문 97편 · 필터 해제
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics. In this work, we instead propos…
3D Object Classification3D Object Recognition3D Point Cloud Classification3D Point Cloud Data Augmentation+4diffConv: Analyzing Irregular Point Clouds with an Irregular View
Standard spatial convolutions assume input data with a regular neighborhood structure. Existing methods typically generalize convolution to the irregular point cloud domain by fixing a regular "view" through e.g. a fixed…
3D Object Classification3D Point Cloud Classification3D Shape ClassificationPointMixer: MLP-Mixer for Point Cloud Understanding
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer. Despite its simplicity compared to transformer, the concept of channel-mixing MLPs and token-mixing MLPs achieves noticeable per…
3D Object Classification3D Point Cloud ClassificationPoint cloud reconstructionSemantic SegmentationBackground-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation
With the proliferation of Lidar sensors and 3D vision cameras, 3D point cloud analysis has attracted significant attention in recent years. After the success of the pioneer work PointNet, deep learning-based methods have…
3D Object ClassificationPoint Cloud SegmentationSegmentationSemantic SegmentationPolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
3D shape representation and its processing have substantial effects on 3D shape recognition. The polygon mesh as a 3D shape representation has many advantages in computer graphics and geometry processing. However, there …
3D Object Classification3D Point Cloud Classification3D Shape Classification3D Shape Recognition+2Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data
Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training. By contrast, unsupervised learning of point cloud data has recei…
3D Object ClassificationClassificationContrastive LearningRepresentation Learning+3LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition
Recently, 3D shape understanding has achieved significant progress due to the advances of deep learning models on various data formats like images, voxels, and point clouds. Among them, point clouds and multi-view images…
3D Object Classification3D Object Retrieval3D Shape Recognition3D Shape Retrieval+1Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis
3D point cloud analysis has drawn a lot of research attention due to its wide applications. However, collecting massive labelled 3D point cloud data is both time-consuming and labor-intensive. This calls for data-efficie…
3D Object Classification3D Part Segmentation3D Point Cloud ClassificationNatural Language Understanding+4ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition
In this paper, we propose Attention Based Decomposition Network (ABD-Net), for point cloud decomposition into basic geometric shapes namely, plane, sphere, cone and cylinder. We show improved performance of 3D object cla…
3D Object ClassificationDense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification
This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic processing units and represent a 3D mesh as …
3D Object ClassificationDescriptiveSemantic SegmentationCross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation
Self-attention mechanism recently achieves impressive advancement in Natural Language Processing (NLP) and Image Processing domains. And its permutation invariance property makes it ideally suitable for point cloud proce…
3D Object ClassificationPoint Cloud SegmentationRepresentation LearningSceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment giv…
3D Object Classification3d scene graph generationGraph Neural NetworkPanoptic Segmentation+3Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme
While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of annotation necessary to approa…
3D Object ClassificationGeneral ClassificationObjectSelf-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based m…
3D Object Classification3D Object RecognitionMULTI-VIEW LEARNINGObject+4Regularization Strategy for Point Cloud via Rigidly Mixed Sample
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processin…
3D Object Classification3D Point Cloud ClassificationData AugmentationPoint Cloud ClassificationSpherical Transformer: Adapting Spherical Signal to CNNs
Convolutional neural networks (CNNs) have been widely used in various vision tasks, e.g. image classification, semantic segmentation, etc. Unfortunately, standard 2D CNNs are not well suited for spherical signals such as…
3D Object ClassificationGeneral Classificationimage-classificationImage Classification+1Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a power…
3D Object ClassificationSuper-ResolutionLearning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat area of 3D objects, such as the boundary an…
3D Object Classification3D Part Segmentation3D Point Cloud ClassificationPoint Cloud Classification+1Classification of Single-View Object Point Clouds
Object point cloud classification has drawn great research attention since the release of benchmarking datasets, such as the ModelNet and the ShapeNet. These benchmarks assume point clouds covering complete surfaces of o…
3D Object Classification6D Pose Estimation using RGBBenchmarkingClassification+5I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
3D object classification has attracted appealing attentions in academic researches and industrial applications. However, most existing methods need to access the training data of past 3D object classes when facing the co…
3D Object ClassificationFairnessIncremental LearningObject