Papers 3D Shape Classification
“3D Shape Classification” 태그가 달린 논문 85편 · 필터 해제
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds
Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds. Nevertheless, existing preeminent point cloud backbones usuall…
3D Shape Classificationgraph constructionObject RecognitionPosition+2DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion
Point cloud processing is a challenging task due to its sparsity and irregularity. Prior works introduce delicate designs on either local feature aggregator or global geometric architecture, but few combine both advantag…
3D Part Segmentation3D Point Cloud Classification3D Shape ClassificationScene Segmentation+2PolyNet: 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+2TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network
Point cloud is one of the widely used techniques for representing and storing 3D geometric data. In the past several methods have been proposed for processing point clouds. Methods such as PointNet and FoldingNet have sh…
3D Reconstruction3D Shape ClassificationPoint Cloud CompletionContrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views
View-based deep learning models have shown the capability to learn 3D shape descriptors with superior performance on 3D shape recognition, classification, and retrieval. Most popular techniques often leverage the class l…
3D Shape Classification3D Shape Recognition3D Shape RepresentationContrastive Learning+2Adaptive Wavelet Transformer Network for 3D Shape Representation Learning
We present a novel method for 3D shape representation learning using multi-scale wavelet decomposition. Distinct from previous works that either decompose 3D shapes into complimentary components at a single scale, or nai…
3D Shape Classification3D Shape RepresentationRepresentation LearningPOINTVIEW-GCN: 3D SHAPE CLASSIFICATION WITH MULTI-VIEW POINT CLOUDS
We address 3D shape classification with partial point cloud inputs captured from multiple viewpoints around the object. Different from existing methods that perform classification on the complete point cloud by first re…
3D Point Cloud Classification3D Shape ClassificationClassificationObjectSpatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by ca…
3D Object Detection3D Point Cloud Classification3D Point Cloud Linear Classification3D Semantic Segmentation+8Point Cloud Learning with Transformer
Remarkable performance from Transformer networks in Natural Language Processing promote the development of these models in dealing with computer vision tasks such as image recognition and segmentation. In this paper, we …
3D Shape ClassificationRepresentation LearningSegmentationSemantic SegmentationPotential Convolution: Embedding Point Clouds into Potential Fields
Recently, various convolutions based on continuous or discrete kernels for point cloud processing have been widely studied, and achieve impressive performance in many applications, such as shape classification, scene seg…
3D Shape ClassificationScene SegmentationLearning Equivariant Representations
State-of-the-art deep learning systems often require large amounts of data and computation. For this reason, leveraging known or unknown structure of the data is paramount. Convolutional neural networks (CNNs) are succes…
3D Shape ClassificationGeneral Classificationimage-classificationImage Classification+3MVTN: Multi-View Transformation Network for 3D Shape Recognition
Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance on 3D shape recognition. Those methods learn different ways to aggregate information from multiple views. However, the c…
3D Classification3D Object Retrieval3D Point Cloud Classification3D Shape Classification+4RocNet: Recursive Octree Network for Efficient 3D Deep Representation
We introduce a deep recursive octree network for the compression of 3D voxel data. Our network compresses a voxel grid of any size down to a very small latent space in an autoencoder-like network. We show results for com…
3D Reconstruction3D Shape Classification3D Shape ReconstructionLearning Attentive and Hierarchical Representations for 3D Shape Recognition
This paper proposes a novel method for 3D shape representation learning, namely Hyperbolic Embedded Attentive Representation (HEAR). Different from existing multi-view based methods, HEAR develops a unified framework to …
3D Shape Classification3D Shape Recognition3D Shape Representation3D Shape Retrieval+2View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis
View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenge for view-based approach is how to aggregate multi-view feat…
3D Shape Classification3D Shape RecognitionRetrievalUnsupervised Deep Shape Descriptor With Point Distribution Learning
Deep learning models have achieved great success in supervised shape descriptor learning for 3D shape retrieval, classification, and correspondence. However, the unsupervised shape descriptor calculated via deep learning…
3D Shape Classification3D Shape RetrievalGeneral ClassificationRetrievalFine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shape classification have rarely been explor…
3D Shape ClassificationGeneral ClassificationRegion ProposalSemantic Part DetectionExtending DeepSDF for automatic 3D shape retrieval and similarity transform estimation
Recent advances in computer graphics and computer vision have found successful application of deep neural network models for 3D shapes based on signed distance functions (SDFs) that are useful for shape representation, r…
3D Shape Classification3D Shape RetrievalModel CompressionRetrievalJoint Supervised and Self-Supervised Learning for 3D Real-World Challenges
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent a…
3D Shape ClassificationGeneral ClassificationSegmentationSelf-Supervised Learning+1Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are…
3D Part Segmentation3D Shape Classification3D Shape Recognition3D Shape Retrieval+2