# GeoCode: Interpretable Shape Programs

arXiv: https://arxiv.org/abs/2212.11715 · HTML: https://mlsanity.com/paper/2212.11715/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2022.

## Built on by (most cited first, 16 of 16)

- [ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives](https://mlsanity.com/paper/2305.05661/) (2023)
- [LL3M: Large Language 3D Modelers](https://mlsanity.com/paper/2508.08228/) (2025)
- [InstructP2P: Learning to Edit 3D Point Clouds with Text Instructions](https://mlsanity.com/paper/2306.07154/) (2023)
- [Improving Unsupervised Visual Program Inference with Code Rewriting Families](https://mlsanity.com/paper/2309.14972/) (2023)
- [SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling](https://mlsanity.com/paper/2306.06088/) (2023)
- [DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation](https://mlsanity.com/paper/2412.15200/) (2024)
- [PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction](https://mlsanity.com/paper/2404.10620/) (2024)
- [GroundUp: Rapid Sketch-Based 3D City Massing](https://mlsanity.com/paper/2407.12739/) (2024)
- [Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding](https://mlsanity.com/paper/2503.18578/) (2025)
- [WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction](https://mlsanity.com/paper/2505.04813/) (2025)
- [Deep Sketch-Based 3D Modeling: A Survey](https://mlsanity.com/paper/2603.03287/) (2026)
- [Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition](https://mlsanity.com/paper/2307.02615/) (2023)
- [SciTextures: Collecting and Connecting Visual Patterns, Models, and Code Across Science and Art](https://mlsanity.com/paper/2511.01817/) (2025)
- [Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy](https://mlsanity.com/paper/2412.12463/) (2024)
- [Coding the Visual World: From Image to Simulation Using Vision Language Models](https://mlsanity.com/paper/2601.05344/) (2026)
- [Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation](https://mlsanity.com/paper/2604.13549/) (2026)

## Abstract (arXiv)

The task of crafting procedural programs capable of generating structurally valid 3D shapes easily and intuitively remains an elusive goal in computer vision and graphics. Within the graphics community, generating procedural 3D models has shifted to using node graph systems. They allow the artist to create complex shapes and animations through visual programming. Being a high-level design tool, they made procedural 3D modeling more accessible. However, crafting those node graphs demands expertise and training. We present GeoCode, a novel framework designed to extend an existing node graph system and significantly lower the bar for the creation of new procedural 3D shape programs. Our approach meticulously balances expressiveness and generalization for part-based shapes. We propose a curated set of new geometric building blocks that are expressive and reusable across domains. We showcase three innovative and expressive programs developed through our technique and geometric building blocks. Our programs enforce intricate rules, empowering users to execute intuitive high-level parameter edits that seamlessly propagate throughout the entire shape at a lower level while maintaining its validity. To evaluate the user-friendliness of our geometric building blocks among non-experts, we conducted a user study that demonstrates their ease of use and highlights their applicability across diverse domains. Empirical evidence shows the superior accuracy of GeoCode in inferring and recovering 3D shapes compared to an existing competitor. Furthermore, our method demonstrates superior expressiveness compared to alternatives that utilize coarse primitives. Notably, we illustrate the ability to execute controllable local and global shape manipulations.