Papers 3D Object Classification
“3D Object Classification” 태그가 달린 논문 97편 · 필터 해제
A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification
Voxel-based 3D object classification has been thoroughly studied in recent years. Most previous methods convert the classic 2D convolution into a 3D form that will be further applied to objects with binary voxel represen…
3D Object ClassificationClassificationGeneral ClassificationPoint Transformer
In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both represen…
3D Object Classification3D Part Segmentation3D Point Cloud ClassificationSemantic SegmentationCascaded Refinement Network for Point Cloud Completion with Self-supervision
Point clouds are often sparse and incomplete, which imposes difficulties for real-world applications. Existing shape completion methods tend to generate rough shapes without fine-grained details. Considering this, we int…
3D Object ClassificationPoint Cloud CompletionWeight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks
We propose novel approaches for simultaneously identifying important weights of a convolutional neural network (ConvNet) and providing more attention to the important weights during training. More formally, we identify t…
3D Action Recognition3D Classification3D Object Classification3D Semantic Segmentation+9Generalized Multi-view Shared Subspace Learning using View Bootstrapping
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks. In this context, two open research questions remain:…
3D Object ClassificationFace RecognitionMULTI-VIEW LEARNINGRobust Face RecognitionGlobal-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
Local and global patterns of an object are closely related. Although each part of an object is incomplete, the underlying attributes about the object are shared among all parts, which makes reasoning the whole object fro…
3D Object ClassificationGeneral ClassificationObjectRepresentation LearningFPConv: Learning Local Flattening for Point Convolution
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis. Unlike previous methods, FPConv doesn't require transforming to intermediate representation like 3D grid or graph and …
3D Object ClassificationScene SegmentationInSphereNet: a Concise Representation and Classification Method for 3D Object
In this paper, we present an InSphereNet method for the problem of 3D object classification. Unlike previous methods that use points, voxels, or multi-view images as inputs of deep neural network (DNN), the proposed meth…
3D Object ClassificationClassificationGeneral ClassificationL3DOC: Lifelong 3D Object Classification
3D object classification has been widely-applied into both academic and industrial scenarios. However, most state-of-the-art algorithms are facing with a fixed 3D object classification task set, which cannot well tackle …
3D geometry3D Object Classification3D Object RecognitionClassification+4Data-Free Point Cloud Network for 3D Face Recognition
Point clouds-based Networks have achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, 3D face recognition method which directly consume point…
3D Object ClassificationFace RecognitionSemantic ParsingMulti-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text
We investigate the impact of using emotional patterns identified by the clinical practitioners and computational linguists to enhance the prediction capabilities of a mental illness detection (in our case depression and …
3D Object ClassificationMulti-class ClassificationMulti-Task LearningAddressing the Sim2Real Gap in Robotic 3D Object Classification
Object classification with 3D data is an essential component of any scene understanding method. It has gained significant interest in a variety of communities, most notably in robotics and computer graphics. While the ad…
3D Object ClassificationClassificationGeneral ClassificationObject+1Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geometric relationships in the data. Similar t…
3D Instance Segmentation3D Object Classification3D Part SegmentationPoint Cloud Classification+2Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classificat…
3D Object Classification3D Point Cloud ClassificationClassificationGeneral Classification+2Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition
We present a generic, flexible and 3D rotation invariant framework based on spherical symmetry for point cloud recognition. By introducing regular icosahedral lattice and its fractals to approximate and discretize sphere…
3D Object Classification3D Part SegmentationGeneral ClassificationRetrievalClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis
Current neural networks for 3D object recognition are vulnerable to 3D rotation. Existing works mostly rely on massive amounts of rotation-augmented data to alleviate the problem, which lacks solid guarantee of the 3D ro…
3D Object Classification3D Object RecognitionClusteringObject RecognitionOctree guided CNN with Spherical Kernels for 3D Point Clouds
We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds. The network architecture capitalizes on the sparse nature of irregular point…
3D Object Classification3D Part SegmentationTranslationExtending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
3D object classification and segmentation using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust ag…
3D Object ClassificationGeneral Classificationimage-classificationImage Classification3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence o…
3D Feature Matching3D Geometry Perception3D Object Classification3D Object Reconstruction+63DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN
Deep learning on point clouds has made a lot of progress recently. Many point cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have shown advantages in accuracy and speed comparing to those usin…
3D geometry3D Object ClassificationClassificationDeep Learning+2