  We introduce SPAFormer, an innovative model designed to overcome the
combinatorial explosion challenge in the 3D Part Assembly (3D-PA) task. This
task requires accurate prediction of each part's pose and shape in sequential
steps, and as the number of parts increases, the possible assembly combinations
increase exponentially, leading to a combinatorial explosion that severely
hinders the efficacy of 3D-PA. SPAFormer addresses this problem by leveraging
weak constraints from assembly sequences, effectively reducing the solution
space's complexity. Since assembly part sequences convey construction rules
similar to sentences being structured through words, our model explores both
parallel and autoregressive generation. It further enhances assembly through
knowledge enhancement strategies that utilize the attributes of parts and their
sequence information, enabling it to capture the inherent assembly pattern and
relationships among sequentially ordered parts. We also construct a more
challenging benchmark named PartNet-Assembly covering 21 varied categories to
more comprehensively validate the effectiveness of SPAFormer. Extensive
experiments demonstrate the superior generalization capabilities of SPAFormer,
particularly with multi-tasking and in scenarios requiring long-horizon
assembly. Codes and model weights will be released at
https://github.com/xuboshen/SPAFormer.
