# 3D Environment Modeling for Falsification and Beyond with Scenic 3.0

arXiv: https://arxiv.org/abs/2307.03325 · HTML: https://mlsanity.com/paper/2307.03325/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2023.

## Built on by (most cited first, 6 of 6)

- [LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation](https://mlsanity.com/paper/2411.00773/) (2024)
- [ScenicProver: A Framework for Compositional Probabilistic Verification of Learning-Enabled Systems](https://mlsanity.com/paper/2511.02164/) (2025)
- [Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems](https://mlsanity.com/paper/2311.09784/) (2023)
- [Lumos: Let there be Language Model System Certification](https://mlsanity.com/paper/2512.02966/) (2025)
- [CARJAN: Agent-Based Generation and Simulation of Traffic Scenarios with AJAN](https://mlsanity.com/paper/2508.21411/) (2025)
- [Diffusion-Guided Search via Exponential Tilting (DiffTilt): An Application to Falsification of Safety-Critical Systems](https://mlsanity.com/paper/2607.23134/) (2026)

## Abstract (arXiv)

We present a major new version of Scenic, a probabilistic programming language for writing formal models of the environments of cyber-physical systems. Scenic has been successfully used for the design and analysis of CPS in a variety of domains, but earlier versions are limited to environments which are essentially two-dimensional. In this paper, we extend Scenic with native support for 3D geometry, introducing new syntax which provides expressive ways to describe 3D configurations while preserving the simplicity and readability of the language. We replace Scenic's simplistic representation of objects as boxes with precise modeling of complex shapes, including a ray tracing-based visibility system that accounts for object occlusion. We also extend the language to support arbitrary temporal requirements expressed in LTL, and build an extensible Scenic parser generated from a formal grammar of the language. Finally, we illustrate the new application domains these features enable with case studies that would have been impossible to accurately model in Scenic 2.