Recently, transformer-based methods have dominated 3D instance segmentation,
where mask attention is commonly involved. Specifically, object queries are
guided by the initial instance masks in the first cross-attention, and then
iteratively refine themselves in a similar manner. However, we observe that the
mask-attention pipeline usually leads to slow convergence due to low-recall
initial instance masks. Therefore, we abandon the mask attention design and
resort to an auxiliary center regression task instead. Through center
regression, we effectively overcome the low-recall issue and perform
cross-attention by imposing positional prior. To reach this goal, we develop a
series of position-aware designs. First, we learn a spatial distribution of 3D
locations as the initial position queries. They spread over the 3D space
densely, and thus can easily capture the objects in a scene with a high recall.
Moreover, we present relative position encoding for the cross-attention and
iterative refinement for more accurate position queries. Experiments show that
our approach converges 4x faster than existing work, sets a new state of the
art on ScanNetv2 3D instance segmentation benchmark, and also demonstrates
superior performance across various datasets. Code and models are available at
this https URL

通过引入位置感知设计，利用辅助中心回归任务替代掩码注意力设计，实现了在 3D 实例分割中更快的收敛速度和更准确的位置查询，并在多种数据集上展现出优越的性能。