Papers 3D Object Classification
“3D Object Classification” 태그가 달린 논문 97편 · 필터 해제
A Graph-CNN for 3D Point Cloud Classification
Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph)…
3D Object Classification3D Point Cloud ClassificationClassificationGeneral Classification+1SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection
We propose an efficient Stereographic Projection Neural Network (SPNet) for learning representations of 3D objects. We first transform a 3D input volume into a 2D planar image using stereographic projection. We then pres…
3D Object ClassificationClassificationGeneral ClassificationGPU+2MeshCNN: A Network with an Edge
Polygonal meshes provide an efficient representation for 3D shapes. They explicitly capture both shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate feature…
3D Object Classification3D Part SegmentationCube Engraving ClassificationLearning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs. We represent 3D shapes as multi-layered height-maps (MLH) where at each grid location, we store multiple instances of height…
3D Object ClassificationGeneral ClassificationSpherical Convolutional Neural Network for 3D Point Clouds
We propose a neural network for 3D point cloud processing that exploits `spherical' convolution kernels and octree partitioning of space. The proposed metric-based spherical kernels systematically quantize point neighbor…
3D Object ClassificationGeneral ClassificationTranslationDeep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems
Automated toll systems rely on proper classification of the passing vehicles. This is especially difficult when the images used for classification only cover parts of the vehicle. To obtain information about the whole ve…
3D Object ClassificationClassificationGeneral ClassificationObjectGeneral-Purpose Deep Point Cloud Feature Extractor
Depth sensors used in autonomous driving and gaming systems often report back 3D point clouds. The lack of structure from these sensors does not allow these systems to take advantage of recent advances in convolutiona…
3D Object Classification3D Point Cloud ClassificationAutonomous DrivingObject Tracking+23D Object Classification via Spherical Projections
In this paper, we introduce a new method for classifying 3D objects. Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical pro…
3D Classification3D Object ClassificationClassificationGeneral Classification+1O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis. Built upon the octree representation of 3D shapes, our method takes the average normal vectors of a 3D model sampled in the fine…
3D Object ClassificationGPURetrievalSemantic SegmentationWide and deep volumetric residual networks for volumetric image classification
3D shape models that directly classify objects from 3D information have become more widely implementable. Current state of the art models rely on deep convolutional and inception models that are resource intensive. Resid…
3D Object ClassificationClassificationGeneral Classificationimage-classification+2Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion. Through the use of skip-connections, our model can successfully l…
3D Object Classification3D Object Recognition3D Reconstruction3D Shape Generation+1Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
A number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows …
3D Object Classification3D Point Cloud ClassificationClassificationGeneral Classification+2ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available -- current datas…
3D Object ClassificationGeneral ClassificationRetrievalScene Understanding+2OctNet: Learning Deep 3D Representations at High Resolutions
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, …
3D Object ClassificationGeneral ClassificationVocal Bursts Intensity PredictionFusionNet: 3D Object Classification Using Multiple Data Representations
High-quality 3D object recognition is an important component of many vision and robotics systems. We tackle the object recognition problem using two data representations, to achieve leading results on the Princeton Model…
3D Object Classification3D Object RecognitionClassificationGeneral Classification+2RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known…
3D Object ClassificationObjectObject CategorizationPose EstimationBlock Coordinate Descent for Sparse NMF
Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural me…
3D Object Classification