SOTAVerified

3D Object Classification

3D Object Classification is the task of predicting the class of a 3D object point cloud. It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric.

Image: Sedaghat et al

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–93 of 93 papers

TitleStatusHype
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces—0
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting—0
Improving Normalization with the James-Stein Estimator—0
L3DOC: Lifelong 3D Object Classification—0
MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction—0
Octree guided CNN with Spherical Kernels for 3D Point Clouds—0
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding—0
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation—0
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives—0
Real-time object detection and tracking using flash LiDAR imagery—0
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme—0
Spherical Convolutional Neural Network for 3D Point Clouds—0
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition—0
Spherical Transformer: Adapting Spherical Signal to CNNs—0
SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection—0
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity—0
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting—0
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data—0
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud ClassifiersCode0
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on GraphsCode0
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised ViewpointsCode0
OctNet: Learning Deep 3D Representations at High ResolutionsCode0
Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid ApproachCode0
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose RestorationCode0
MeshCNN: A Network with an EdgeCode0
Learning a Hierarchical Latent-Variable Model of 3D ShapesCode0
Densely Connected G-invariant Deep Neural Networks with Signed Permutation RepresentationsCode0
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point CloudCode0
Spherical Kernel for Efficient Graph Convolution on 3D Point CloudsCode0
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional NetworksCode0
InSphereNet: a Concise Representation and Classification Method for 3D ObjectCode0
Improved Training for 3D Point Cloud ClassificationCode0
General-Purpose Deep Point Cloud Feature ExtractorCode0
A Fast Hybrid Cascade Network for Voxel-based 3D Object ClassificationCode0
A Graph-CNN for 3D Point Cloud ClassificationCode0
3D Point Capsule NetworksCode0
Show:102550
← PrevPage 2 of 2Next →
All datasetsModelNet40ModelNet103R-ScanRemote Flash LiDAR Vehicles Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OursClassification Accuracy93.6—Unverified
2G3DNet-18 MLP, Fine-Tuned, VoteClassification Accuracy91.7—Unverified
3CrossMoCoClassification Accuracy91.49—Unverified
4O-CNN(6)Classification Accuracy89.9—Unverified
5Spherical KernelClassification Accuracy89.3—Unverified
63D-PointCapsNetClassification Accuracy89.3—Unverified
7ECC (12 votes)Classification Accuracy83.2—Unverified
#ModelMetricClaimedVerifiedStatus
1PolyNetAccuracy94.93—Unverified
2ORIONAccuracy93.8—Unverified
3G3DNet-18 SVM, Fine-Tuned, VoteAccuracy93.1—Unverified
4ECC (12 votes)Accuracy90—Unverified
#ModelMetricClaimedVerifiedStatus
1SceneGraphFusionTop-10 Accuracy0.8—Unverified
23DSSG [Wald2020_3dssg]Top-10 Accuracy0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1YOLO-Xmean average precision0.99—Unverified