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 51–75 of 93 papers

TitleStatusHype
Fast Sparse 3D Convolution Network with VDB—0
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net—0
3D Object Classification via Spherical Projections—0
Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification—0
MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction—0
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text—0
MVImgNet: A Large-scale Dataset of Multi-view Images—0
Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems—0
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity—0
Octree guided CNN with Spherical Kernels for 3D Point Clouds—0
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting—0
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data—0
Data-Free Point Cloud Network for 3D Face Recognition—0
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation—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
Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection—0
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis—0
LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition—0
Beyond local patches: Preserving global–local interactions by enhancing self-attention via 3D point cloud tokenization—0
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation—0
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives—0
Real-time object detection and tracking using flash LiDAR imagery—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
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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