Generative models for 3D shapes represented by hierarchies of parts can generate realistic and diverse sets of outputs. However, existing models suffer from the key practical limitation of modelling shapes holistically and thus cannot perform conditional sampling, i.e. they are not able to generate variants on individual parts of generated shapes without modifying the rest of the shape. This is limiting for applications such as 3D CAD design that involve adjusting created shapes at multiple levels of detail. To address this, we introduce LSD-StructureNet, an augmentation to the StructureNet architecture that enables re-generation of parts situated at arbitrary positions in the hierarchies of its outputs. We achieve this by learning individual, probabilistic conditional decoders for each hierarchy depth. We evaluate LSD-StructureNet on the PartNet dataset, the largest dataset of 3D shapes represented by hierarchies of parts. Our results show that contrarily to existing methods, LSD-StructureNet can perform conditional sampling without impacting inference speed or the realism and diversity of its outputs.
翻译：3D 形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形色色的模型。然而,现有模型在整体上受到建模形形形形形色色的关键实际限制,因此无法进行有条件取样,也就是说,它们无法在不修改形状其余部分的情况下,在生成形状的每个部分产生不同的变体。这限制了诸如3D CAD 型形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形形
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