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We address the problem of generating a sequence of LEGO brick assembly withhigh-fidelity structures, satisfying physical constraints between bricks. Theassembly problem is challenging since the number of possible structuresincreases exponentially with the number of available bricks, complicating thephysical constraints to satisfy across bricks. To tackle this problem, ourmethod performs a brick structure assessment to predict the next brick positionand its confidence by employing a U-shaped sparse 3D convolutional network. Theconvolution filter efficiently validates physical constraints in aparallelizable and scalable manner, allowing to process of different bricktypes. To generate a novel structure, we devise a sampling strategy todetermine the next brick position by considering attachable positions underphysical constraints. Instead of using handcrafted brick assembly datasets, ourmodel is trained with a large number of 3D objects that allow to create a newhigh-fidelity structure. We demonstrate that our method successfully generatesdiverse brick structures while handling two different brick types andoutperforms existing methods based on Bayesian optimization, graph generativemodel, and reinforcement learning, all of which are limited to a single bricktype.

Sequential Brick Assembly with Efficient Constraint Satisfaction