| ai4ce/DeepMapping |
191 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
other |
Python |
| [CVPR2019 Oral] Self-supervised Point Cloud Map Estimation |
| MohamedAfham/CrossPoint |
179 |
|
0 |
0 |
almost 3 years ago |
0 |
|
9 |
|
Python |
| Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022) |
| antao97/UnsupervisedPointCloudReconstruction |
154 |
|
0 |
0 |
almost 4 years ago |
0 |
|
0 |
mit |
Python |
| Experiments on unsupervised point cloud reconstruction. |
| xiaoaoran/3d_url_survey |
100 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
|
|
| (TPAMI2023) Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey |
| darrenjkt/MS3D |
89 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
apache-2.0 |
Python |
| Auto-labeling of point cloud sequences for 3D object detection using an ensemble of experts and temporal refinement |
| Zengyi-Qin/Weakly-Supervised-3D-Object-Detection |
85 |
|
0 |
0 |
about 4 years ago |
0 |
|
7 |
mit |
Jupyter Notebook |
| Weakly Supervised 3D Object Detection from Point Clouds (VS3D), ACM MM 2020 |
| eliahuhorwitz/3D-ADS |
53 |
|
0 |
0 |
over 3 years ago |
0 |
|
3 |
mit |
Python |
| Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper. |
| vLAR-group/OGC |
50 |
|
0 |
0 |
over 3 years ago |
0 |
|
1 |
other |
Python |
| 🔥OGC in PyTorch (NeurIPS 2022) |
| vLAR-group/GrowSP |
41 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
|
Python |
| 🔥GrowSP in PyTorch (CVPR 2023) |
| sulaimanvesal/PointCloudUDA |
19 |
|
0 |
0 |
over 3 years ago |
0 |
|
3 |
mit |
Python |
| [IEEE-TMI 2021] This is our PyTorch implementation for Adapt Everywhere paper on unsupervised domain adaptation using entropy and point-cloud paper. |
