解决3D实例分割中大物体过分割问题，提升掩码精度
MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation
- 多尺度特征+双注意力机制，捕捉更丰富的空间语义
- 引入框查询与框正则化，缓解掩码预测不可靠问题
- 在ScanNetV2等数据集上超越现有方法，适合高精度场景理解
近期基于Transformer并结合超点的3D实例分割方法广泛应用，但常面临大物体过分割问题，且由超点掩码预测不准确进一步加剧。为此，我们提出MSTA3D框架，利用多尺度特征表示并引入双注意力机制以有效捕获特征。此外，MSTA3D融合框查询与框正则化，在语义查询外提供互补的空间约束。在ScanNetV2、ScanNet200和S3DIS数据集上的实验表明，该方法优于当前最先进的3D实例分割方法。
原文摘要 · Abstract (English)
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.
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