Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers (2501.17044v2)

Abstract: We generate abstractions of buildings, reflecting the essential aspects of their geometry and structure, by learning to invert procedural models. We first build a dataset of abstract procedural building models paired with simulated point clouds and then learn the inverse mapping through a transformer. Given a point cloud, the trained transformer then infers the corresponding abstracted building in terms of a programmatic language description. This approach leverages expressive procedural models developed for gaming and animation, and thereby retains desirable properties such as efficient rendering of the inferred abstractions and strong priors for regularity and symmetry. Our approach achieves good reconstruction accuracy in terms of geometry and structure, as well as structurally consistent inpainting.

• The paper introduces a transformer-based inversion method to generate high-fidelity 3D abstractions from procedural building models.
• The methodology integrates synthetic datasets with structural priors, achieving over 95% accuracy in reconstructing key building features.
• Results show robust performance on noise-augmented point clouds with high recall and precision, enhancing efficiency in 3D mapping applications.

Insights and Implications of "Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers"

In the paper "Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformers," the authors present a novel method for generating abstract representations of buildings by performing an inversion of procedural models using transformer architectures. This approach is significant for applications requiring efficient and high-level abstraction of geometric structures, such as 3D mapping and synthetic environment generation for AI training. The paper's methodology leverages the procedural capabilities developed predominantly within the gaming and animation domains to achieve high fidelity and structurally accurate abstractions from point cloud data.

Methodology Overview

The research advances the field by specifying a robust framework that consists of several intricately connected components:

1. Dataset Generation: The authors generate a synthetic dataset that combines procedural building models with corresponding simulated point clouds. This dataset facilitates a comprehensive understanding of the relationship between point clouds and building abstractions.
2. Transformer-Based Model: The core of the methodology is a transformer network trained to approximate the inverse mapping from point clouds to procedural abstractions. By employing transformers, a model architecture known for its scalability and efficiency, the research capitalizes on their ability to model complex distributions like that of building geometries.
3. Integration of Structural Knowledge: The approach integrates prior structural knowledge inherent in the procedural models into a learning framework, enhancing the reconstruction accuracy of geometric and structural details.

A Bayesian framework guides the extensive process, treating abstractions as parameterizations $\theta$ of buildings given point clouds $x$. In this context, the inference model $q(\theta|x)$ is optimized to approximate the posterior distribution, taking advantage of both geometric fit and structural regularity inherent to procedural models.

Results and Performance

The authors report high accuracy in reconstructing key building features from point clouds across various metrics. Structural evaluations reveal that the model correctly identifies the number of storeys and facade structures with over 95% accuracy. The high recall (98.7%) and precision (99.4%) on assets further highlight the model's capability to maintain diverse and accurate asset distributions. Additionally, geometric evaluations indicate minimal deviation, even with noise-augmented point clouds, demonstrating robust performance under varying input conditions.

Practical and Theoretical Implications

The practical implications of this work lie in its application to domains where the rapid and efficient rendering of architectural data is crucial. By translating detailed geometric data into abstract procedural descriptions, the process becomes both computationally efficient and versatile, allowing for swift rendering adjustments and consistent abstraction representation. Given this alignment with procedural models, the potential for integration with real-world data is apparent, though contingent on advancements in procedural flexibility.

From a theoretical perspective, the paper sheds light on the effectiveness of introducing regularity priors via procedural models within the inverse design framework. By incorporating such priors, the inference model can adeptly infer missing data, suggesting a symbiosis between learned structures and innate procedural symmetries. This approach underscores the utility of simulation-based training and highlights potential avenues for augmenting procedural models to achieve broader applicability.

Future Directions

As the research community continues to explore and expand the horizons of AI and procedural modeling, future developments could focus on enhancing procedural models to accommodate more diverse building styles and real-world variations. Such flexibility could potentially resolve current limitations associated with the rigidity of procedural models, allowing for more scalable applications. Additionally, addressing domain shifts, particularly in converting simulated to real-world point clouds, remains a salient area for advancing the applicability of this framework.

In conclusion, the paper effectively demonstrates the use of transformers in capturing the abstract essence of building structures, paving the way for innovations in the abstraction and rendering of 3D models. This research presents valuable paradigms for the intersection of AI, geometry processing, and procedural modeling, encouraging further exploration and refinement in these interconnected domains.