Superpoint Transformer (SPT) is a superpoint-based transformer architecture that efficiently performs semantic segmentation on large-scale 3D scenes. Install.sh · Issues 2 · Pull requests 1

We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes.

This tutorial will demonstrate how to use Superpoint Transformer (SPT) on your own point cloud data. In our running example, we will use a large point cloud ...

This repo is a pytorch implementation for these methods and aims to compare them under a fair setting. Currently, all three methods are implemented, while ...

Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and ...

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry ...

This is the configuration class to store the configuration of a [`SuperPointForKeypointDetection`]. It is used to instantiate a. SuperPoint model according ...

7 нояб. 2022 г. · This paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer.

Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - transformers/src/transformers/models/superpoint/modeling_superpoint.py ...

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