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

Showing 5175 of 93 papers

TitleStatusHype
Regularization Strategy for Point Cloud via Rigidly Mixed SampleCode1
Spherical Transformer: Adapting Spherical Signal to CNNs0
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis0
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point CloudCode1
Classification of Single-View Object Point Clouds0
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting0
A Fast Hybrid Cascade Network for Voxel-based 3D Object ClassificationCode0
Point TransformerCode1
Cascaded Refinement Network for Point Cloud Completion with Self-supervisionCode1
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural NetworksCode1
Generalized Multi-view Shared Subspace Learning using View Bootstrapping0
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point CloudsCode1
FPConv: Learning Local Flattening for Point ConvolutionCode1
InSphereNet: a Concise Representation and Classification Method for 3D ObjectCode0
L3DOC: Lifelong 3D Object Classification0
Data-Free Point Cloud Network for 3D Face Recognition0
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text0
Addressing the Sim2Real Gap in Robotic 3D Object Classification0
Spherical Kernel for Efficient Graph Convolution on 3D Point CloudsCode0
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World DataCode1
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis0
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition0
Octree guided CNN with Spherical Kernels for 3D Point Clouds0
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud ClassifiersCode0
3D Point Capsule NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OursClassification Accuracy93.6Unverified
2G3DNet-18 MLP, Fine-Tuned, VoteClassification Accuracy91.7Unverified
3CrossMoCoClassification Accuracy91.49Unverified
4O-CNN(6)Classification Accuracy89.9Unverified
5Spherical KernelClassification Accuracy89.3Unverified
63D-PointCapsNetClassification Accuracy89.3Unverified
7ECC (12 votes)Classification Accuracy83.2Unverified
#ModelMetricClaimedVerifiedStatus
1PolyNetAccuracy94.93Unverified
2ORIONAccuracy93.8Unverified
3G3DNet-18 SVM, Fine-Tuned, VoteAccuracy93.1Unverified
4ECC (12 votes)Accuracy90Unverified
#ModelMetricClaimedVerifiedStatus
1SceneGraphFusionTop-10 Accuracy0.8Unverified
23DSSG [Wald2020_3dssg]Top-10 Accuracy0.78Unverified
#ModelMetricClaimedVerifiedStatus
1YOLO-Xmean average precision0.99Unverified