A 3D and 2D processing base on glam
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Updated
May 16, 2024 - Rust
A point cloud is a set of data points in space. The points represent a 3D shape or object. Each point has its set of X, Y and Z coordinates.
A 3D and 2D processing base on glam
C++ library which provides data structures & algorithms for working with 3D point cloud data
[ICLR 2024] AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
Open3DIS: Open-vocabulary 3D Instance Segmentation with 2D Mask Guidance (CVPR 2024)
PointMamba: A Simple State Space Model for Point Cloud Analysis
🔥3D点云目标检测&语义分割(深度学习)-SOTA方法,代码,论文,数据集等
[CVPR 2024] Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
[ICML 2023] Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
[ICLR 2023] Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
[IROS 2023] Open-Vocabulary Affordance Detection in 3d Point Clouds
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic Segmentation (WACV 2024)
pyntcloud is a Python library for working with 3D point clouds.
[ICCV 2023] Official implementation for "Label-Guided Knowledge Distillation for Continual Semantic Segmentation on 2D Images and 3D Point Clouds".
Explore the World in 3D: 3DPointCloudLab is your gateway to the fascinating universe of 3D depth maps and point clouds. Whether you're a researcher, developer, or 3D enthusiast, our repository offers a treasure trove of tools, techniques, and insights dedicated to the exploration and manipulation of 3D spatial data.
The official implementation of the "Hypernetwork approach to generating point clouds" paper
ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution (CVPR 2023)
A comprehensive surevy on Multimodal Models in 3D
PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows