3D Point Cloud Linear Classification On
Metrics
Overall Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Overall Accuracy |
|---|---|
| implicit-autoencoder-for-point-cloud-self | 92.1 |
| learning-3d-representations-from-2d-pre | 93.4 |
| adacrossnet-adaptive-dynamic-loss-weighting | 91.4 |
| contrast-with-reconstruct-contrastive-3d | 93.4 |
| pre-training-by-completing-point-clouds | 89.2 |
| crosspoint-self-supervised-cross-modal | 91.2 |
| multi-angle-point-cloud-vae-unsupervised | 88.4 |
| point-m2ae-multi-scale-masked-autoencoders | 92.9 |
| foldingnet-point-cloud-auto-encoder-via-deep | 88.4 |
| self-supervised-learning-of-point-clouds-via | 90.7 |
| context-prediction-for-unsupervised-deep | 90.6 |
| unsupervised-3d-learning-for-shape-analysis | 90.3 |
| so-net-self-organizing-network-for-point | 87.5 |
| spatio-temporal-self-supervised | 90.9 |
| progressive-seed-generation-auto-encoder-for-1 | 90.9 |
| learning-a-probabilistic-latent-space-of | 83.3 |
| point-jepa-a-joint-embedding-predictive | 93.7±0.2 |
| shapellm-universal-3d-object-understanding | 93.6 |
| view-inter-prediction-gan-unsupervised | 90.2 |
| crossmoco-multi-modal-momentum-contrastive | 91.49 |