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 2650 of 93 papers

TitleStatusHype
Densely Connected G-invariant Deep Neural Networks with Signed Permutation RepresentationsCode0
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity0
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose RestorationCode0
Improved Training for 3D Point Cloud ClassificationCode0
HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces0
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding0
MATE: Masked Autoencoders are Online 3D Test-Time LearnersCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives0
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud UnderstandingCode2
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting0
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
diffConv: Analyzing Irregular Point Clouds with an Irregular ViewCode1
PointMixer: MLP-Mixer for Point Cloud UnderstandingCode1
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation0
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape RepresentationCode1
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data0
LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition0
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis0
ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition0
Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification0
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation0
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D SequencesCode1
Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations0
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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