mlsanityData to 7 Oct 2026

Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program

arXiv 2212.12952 ↗First posted 25 Dec 2022cs.CV cs.AIcited by 6

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Abstract (arXiv)

3D shapes have complementary abstractions from low-level geometry to part-based hierarchies to languages, which convey different levels of information. This paper presents a unified framework to translate between pairs of shape abstractions: textitText Longleftrightarrow textitPoint Cloud Longleftrightarrow textitProgram. We propose Neural Shape Compiler to model the abstraction transformation as a conditional generation process. It converts 3D shapes of three abstract types into unified discrete shape code, transforms each shape code into code of other abstract types through the proposed textitShapeCode Transformer, and decodes them to output the target shape abstraction. Point Cloud code is obtained in a class-agnostic way by the proposed textitPointVQVAE. On Text2Shape, ShapeGlot, ABO, Genre, and Program Synthetic datasets, Neural Shape Compiler shows strengths in textitText Longrightarrow textitPoint Cloud, textitPoint Cloud Longrightarrow textitText, textitPoint Cloud Longrightarrow textitProgram, and Point Cloud Completion tasks. Additionally, Neural Shape Compiler benefits from jointly training on all heterogeneous data and tasks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 87 of its 130 references that are in the map. First posted on arXiv 25 Dec 2022.

Builds on

87 of 130 references.

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38 more
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50 most cited of 87 All 87 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 1.

6 papers.