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Found 4 papers, 2 papers with code
Learning to Assemble Geometric Shapes
Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering.
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations.
Fragment Relation Networks for Geometric Shape Assembly
Our model processes the candidate fragments in a permutation-equivariant manner and can generalize to cases with an arbitrary number of fragments and even with a different target object.
Combinatorial 3D Shape Generation via Sequential Assembly
To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework.
