  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
https://github.com/dvlab-research/Mask-Attention-Free-Transformer.
