10 datasets found

A point cloud processing pipeline that transforms the point cloud into a compact, regular grid and performs neural operations on the grid.

Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices.

The dataset used in this paper for 3D point cloud classification and adversarial example generation.

The dataset is used for testing the proposed Generative Point-based NeRF framework.

FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer

The proposed approach enhances the zero-shot classification capability on extremely sparse point clouds.

The Business District dataset is a dataset of 3D point clouds of business districts.

The Residential Area dataset is a dataset of 3D point clouds of residential areas.

The University Sector dataset is a dataset of 3D point clouds of university buildings.

PointConvFormer is a novel point cloud layer that combines ideas from point convolution and transformers.