# Scene reconstruction from ray tracing simulation

**Max. student(s)**: 1-2**Advisor**: Laurent Jacques**Teaching Assistant**: Jérome Eertmans

## Context

In optics, acoustics, or telecommunications, there are many methods to estimate the power of a received signal, depending on the position of the transmitter, that of the receiver, and the geometry of the scene. Among these methods, ray tracing has gained a particular interest in recent years, especially thanks to advances in terms of computing power.

Ray tracing is quite simple to understand: from an emitting source (e.g., a light source, a speaker or a radio antenna), one traces a very large number of rays going in all directions. Each time we encounter an obstacle, we apply a specular reflection^{1}, and we continue until we reach the receiver. If a ray has not reached the target after a fixed number of bounces, it is abandoned. The method described here is similar to ray launching, one of the many ray tracing variants.

## Problem

As one may notice, the number of rays that arrive to the receiver is much lower than the number of rays sent. **Our question is the following:** *can we infer some parameters of the scene just by looking at the rays that we receive?*

Of course, the answer to this question depends on the parameters you know apriori, and those you need to infer. The next section will detail the objectives we thought about.

## Objectives

For this Master thesis, we propose the following steps^{2}:

- From a known 2D scene, a fixed number of reflections, and a known antenna pattern
^{3}, show how you can determine the emitter’s location from the set of rays received at the receiver (what are the necessary conditions in this setting for this to work?); - Study the sparsity / scaristy of the number of rays received, depending on the emitter’s position, or the antenna pattern (i.e., very directive vs omnidirectionnal);
- Do the same as (1), but the unkown is now the reflection coefficient of some wall(s) in the scene;
- Study how uncertainty on some parameter (e.g., you know the position of the receiver with a precision of 1cm) impacts your estimation of unknown parameters;
- Can you infer more information about the emitter (e.g., its radiation pattern) from the knowledge of the scene’s response to a set of known emitter configurations?

## Links

- Ray Tracing Gems from Nvidia (url)
- Book chapter about sound propagation (url)
- Fast and simple algorithm to detect object shadowing (url)

## References

- [1] Chandak, A., Antani, L., Taylor, M., & Manocha, D. (2011). Fast and Accurate Geometric Sound Propagation Using Visibility Computations. Building Acoustics, 18(1–2), 123–144. (doi)
- [2] Geok, T. K., Hossain, F., & Chiat, A. T. W. (2018). A Novel 3D Ray Launching Technique for Radio Propagation Prediction in Indoor Environments. PLOS ONE, 13(8), e0201905. (doi)
- [3] Liu, S., & Manocha, D. (2022). Sound Propagation. In S. Liu & D. Manocha (Eds.), Sound Synthesis, Propagation, and Rendering (pp. 29–43). Cham: Springer International Publishing. (doi)
- [4] Marrs, A., Shirley, P., & Wald, I. (Eds.). (2021). Ray Tracing Gems II: Next Generation Real-Time Rendering with DXR, Vulkan, and OptiX. Berkeley, CA: Apress. (doi)