20 дек. 2017 г. · This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry ...

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

A deep learning-based visual feature detection method that reformulates the interest point detection problem as a supervised dense descriptor prediction task.

6 июн. 2024 г. · We propose a novel framework named Superpoint Gaussian Splatting (SP-GS). Specifically, our framework first employs explicit 3D Gaussians to reconstruct the ...

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 a Tensorflow implementation of SuperPoint: Self-Supervised Interest Point Detection and Description. Superpoint_pytorch.py · MIT license · README.md · Activity

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

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

29 дек. 2021 г. · SuperPoint presents a self-supervised solution using self-training. This is done through creating a large dataset of pseudo-ground truth interest point ...

Learn a real-time feature detector based on self generated data. “Super”point as it creates a superset of an early work “magic point”.