This repo is the official project repository of the paper Point Transformer V3: Simpler, Faster, Stronger and is mainly used for releasing schedules, updating instructions, sharing experiment records (containing model weight), and handling issues.

Overview of Point Transformer V3 (PTv3). Compared to its predecessor, PTv2 [90], our PTv3 shows superiority in the following aspects: 1. Stronger performance. PTv3 achieves state-of-the-art results across a variety of indoor and outdoor 3D perception tasks. 2. Wider receptive field.

We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information.

Our future work will aim at using the graph model to construct local features of the human body to further improve the detection performance . Acknowledgments. This work was supported by National Key Research and Development Program of China under Grant 2018AAA0101602 and National Natural Science Foundation of China (61922030). Data Availability

This repository reproduces Point Transformer. The codebase is provided by the first author of Point Transformer. For shape classification and part segmentation, please use paconv-codebase branch. After some testing, we will merge it into the master branch. For now, please use paconv-codebase branch.

We design self-attention layers for point clouds and use these to construct self-attention networks for tasks such as semantic scene segmentation, object part segmentation, and object classification. Our Point Transformer design improves upon prior work across domains and tasks.

Dec 15, 2023 · Therefore, we present Point Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the overall performance after scaling, such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns.

Jun 2, 2024 · PT is a 3D point cloud processing network that utilizes ‘Self-Attention’. PT can perform Semantic Segmentation, Part Segmentation and Object Classification of 3D point clouds. The transformer...

Jan 1, 2022 · We propose a novel end-to-end graph autoencoders model for the attributed graph. The proposed model can reconstruct both the graph structure and node attributes. The graph encoder is a completely low-pass filter. The graph decoder is a completely high-pass filter. Show the effectiveness of the proposed model.

Feb 1, 2023 · Specifically, graph-based modifications of the MBO scheme are integrated with state-of-the-art techniques, including a home-made transformer and an autoencoder, in order to deal with scarcely-labeled data sets. In addition, a consensus technique is detailed. The proposed models are validated using five benchmark data sets.