We introduce COALESCE, the first data-driven framework for component-based
shape assembly which employs deep learning to synthesize part connections. To
handle geometric and topological mismatches between parts, we remove the
mismatched portions via erosion, and rely on a joint synthesis step, which is
learned from data, to fill the gap and arrive at a natural and plausible part
joint. Given a set of input parts extracted from different objects, COALESCE
automatically aligns them and synthesizes plausible joints to connect the parts
into a coherent 3D object represented by a mesh. The joint synthesis network,
designed to focus on joint regions, reconstructs the surface between the parts
by predicting an implicit shape representation that agrees with existing parts,
while generating a smooth and topologically meaningful connection. We employ
test-time optimization to further ensure that the synthesized joint region
closely aligns with the input parts to create realistic component assemblies
from diverse input parts. We demonstrate that our method significantly
outperforms prior approaches including baseline deep models for 3D shape
synthesis, as well as state-of-the-art methods for shape completion.

COALESCE 是一个利用深度学习综合部件连接的数据驱动框架，可处理部件之间的几何和拓扑不匹配，进而生成自然和明显的部件连接，最终将其组合成一致的 3D 对象。该方法在形状合成方面明显优于现有方法，包括基准深度模型和 State-of-the-Art 方法。