Datasets:
license: scannet
[CVPR 2025] GFS-VL: Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
Overview
GFS-VL is a novel framework proposed in our CVPR 2025 paper: Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model.
Our approach leverages the synergy between:
- Dense but noisy pseudo-labels from 3D Vision-Language Models
- Precise yet sparse few-shot samples
by maximizing the strengths of both data sources for effective generalized few-shot 3D point cloud segmentation.
Benchmark Datasets
In the papaer, we introduce two new challenging GFS-PCS benchmarks with diverse novel classes for comprehensive generalization evaluation.
This repository contains the two novel GFS-PCS benchmarks:
- ScanNet200: Our GFS benchmark based on ScanNet200, also including the original ScanNet labels
- ScanNet++: Our GFS benchmark based on ScanNet++
These benchmarks lay a solid foundation for real-world GFS-PCS advancements.
Note: To use these datasets, you must first agree to the terms and apply for access. Please refer to ScanNet200 and ScanNet++ for instructions.
Dataset Structure
Each dataset in this repository is organized as follows:
- Splits: Train, validation, and test sets.
- Registration Data List: For both 1-shot and 5-shot scenarios, each includes five randomly generated registration sets.
Usage
For detailed usage instructions, model implementation, and training code, please refer to our GitHub repository.
Pre-trained Model Weights
The complete GFS-VL pre-trained model weights can be found in our model weights repository.
Citation
If you find our work useful, please consider citing our paper:
@inproceedings{an2025generalized,
title={Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model},
author={An, Zhaochong and Sun, Guolei and Liu, Yun and Li, Runjia and Han, Junlin and Konukoglu, Ender and Belongie, Serge},
booktitle=CVPR,
year={2025}
}