@inproceedings{
duc2024multiscale,
title={Multi-scale Twin-attention for 3D Instance Segmentation},
author={Tran Dang Trung Duc and Byeongkeun Kang and Yeejin Lee},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=pXY8tluwLV}
}
Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, especially noticeable with large objects. Additionally, unreliable mask predictions stemming from superpoint mask prediction further compound this issue. To address these challenges, we propose a novel framework called MSTA3D. It leverages multi-scale feature representation and introduces twin-attention mechanisms to effectively capture them. Furthermore, MSTA3D integrates a box query with a box regularizer, offering a complementary spatial constraint alongside semantic queries. Experimental evaluations on ScanNetV2, ScanNet200, and S3DIS datasets demonstrate that our approach surpasses state-of-the-art 3D instance segmentation methods.
Multi-scale Twin-attention for 3D Instance Segmentation
Tran Dang Trung Duc Byeongkeun Kang Yeejin Lee