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FlowNet3D: Learning scene flow in 3D point clouds.
Learning scene flow in 3D point clouds. -
STPLS3D: A large-scale synthetic and real aerial photogrammetry 3D point clou...
STPLS3D: A large-scale synthetic and real aerial photogrammetry 3D point cloud dataset -
Open3D: A modern library for 3D data processing
Open3D: A modern library for 3D data processing -
ObjectNet3D: A large scale dataset for 3D object recognition
3D object recognition dataset -
ShapeNet: A large scale dataset for 3D object recognition
3D object recognition dataset -
Generative Models for 3D Objects
Generative models for 3D objects -
PointPillars Dataset
The PointPillars dataset is a benchmark for object detection in 3D point clouds. -
Diffusion Probabilistic Models for 3D Point Cloud Generation
We propose a novel probabilistic generative model for point clouds, taking inspiration from the diffusion process in non-equilibrium thermodynamics. -
Argoverse 2
The Argoverse 2 motion forecasting dataset contains 250,000 driving scenarios, each 11 seconds long. These scenarios cover 6 geographical regions and represent 763 total hours... -
One Million Scenes for Autonomous Driving (ONCE)
The ONCE dataset comprises 1 million LiDAR scenes and 7 million corresponding camera images. -
ScanNet200
Diff2Scene uses ScanNet, Matterport3D, ScanNet200 and Replica for open-vocabulary 3D semantic segmentation and visual grounding tasks. -
ModelNet10 and ModelNet40
The dataset used for point cloud classification and segmentation tasks. -
Matterport3D dataset
The Matterport3D dataset contains scans of real-world environments such as apartments, offices, and churches. -
Point Attention Network for Semantic Segmentation of 3D Point Clouds
Convolutional Neural Networks (CNNs) have performed extremely well on data represented by regularly arranged grids such as images. However, directly leveraging the classic... -
3D Scene Graphs
The 3D scene graph dataset contains 3D scene graphs with semantic entities, attributes, and relationships. -
PointConv: Deep Convolutional Networks on 3D Point Clouds
3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. PointConv can be applied on point clouds to build deep convolutional networks. -
RueMonge2014
The dataset used in this paper for 3D point cloud classification and semantic segmentation tasks.
