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 a twin-attention mechanism 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.
@article{arxiv.2411.01781,
title = {MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation},
author = {Duc Dang Trung Tran and Byeongkeun Kang and Yeejin Lee},
journal= {arXiv preprint arXiv:2411.01781},
year = {2024}
}