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

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Showing 1–50 of 93 papers

TitleStatusHype
PCP-MAE: Learning to Predict Centers for Point Masked AutoencodersCode2
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D PriorsCode2
Uni3D: Exploring Unified 3D Representation at ScaleCode2
PointLLM: Empowering Large Language Models to Understand Point CloudsCode2
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud UnderstandingCode2
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point SetsCode1
DC3DO: Diffusion Classifier for 3D ObjectsCode1
Open-Pose 3D Zero-Shot Learning: Benchmark and ChallengesCode1
Point Cloud Self-supervised Learning via 3D to Multi-view Masked AutoencoderCode1
Extending Multi-modal Contrastive RepresentationsCode1
Exploiting Inductive Bias in Transformer for Point Cloud Classification and SegmentationCode1
MATE: Masked Autoencoders are Online 3D Test-Time LearnersCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
diffConv: Analyzing Irregular Point Clouds with an Irregular ViewCode1
PointMixer: MLP-Mixer for Point Cloud UnderstandingCode1
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape RepresentationCode1
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D SequencesCode1
Regularization Strategy for Point Cloud via Rigidly Mixed SampleCode1
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point CloudCode1
Point TransformerCode1
Cascaded Refinement Network for Point Cloud Completion with Self-supervisionCode1
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural NetworksCode1
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point CloudsCode1
FPConv: Learning Local Flattening for Point ConvolutionCode1
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World DataCode1
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape AnalysisCode1
ScanNet: Richly-annotated 3D Reconstructions of Indoor ScenesCode1
Block Coordinate Descent for Sparse NMFCode1
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network—0
Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation—0
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net—0
Beyond local patches: Preserving global–local interactions by enhancing self-attention via 3D point cloud tokenization—0
MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction—0
Formula-Supervised Visual-Geometric Pre-training—0
Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
Real-time object detection and tracking using flash LiDAR imagery—0
Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid ApproachCode0
Improving Normalization with the James-Stein Estimator—0
Fast Sparse 3D Convolution Network with VDB—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point CloudCode0
MVImgNet: A Large-scale Dataset of Multi-view Images—0
Densely Connected G-invariant Deep Neural Networks with Signed Permutation RepresentationsCode0
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity—0
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 Surfaces—0
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding—0
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives—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