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/library/oar/handle/123456789/106973| Title: | A fast approximate light transport method for ray tracing |
| Authors: | Scerri, Luke Bjorn (2022) |
| Keywords: | Computer graphics Light -- Scattering |
| Issue Date: | 2022 |
| Citation: | Scerri, L.B. (2022). A fast approximate light transport method for ray tracing (Bachelor's dissertation). |
| Abstract: | In computer graphics, rendering is the process of transforming a scene from a numerical format into a picture that can be visualised and displayed on a screen. The computation of scattering when light interacts with a surface is dependent on the surface material. For some materials this operation is straightforward and computationally inexpensive (e.g. perfect mirrors), while for others (e.g. translucent materials such as milk and marble), it is prohibitively expensive. This operation requires computing the closest point of intersection with a polygon in the light path, for large scenes with tens of millions of polygons, this operation is very expensive. Spatial data structures are often used to group polygons in the scene and accelerate ray intersection tests. A signed distance field (SDF) is a continuous scalar field of values that represent the closest distance to the surface of an object. SDFs are commonly used in collision detection and other physical simulations. In this work, we look at the use of signed distance fields to accelerate point-to-point intersection tests in a scene, thus reducing the time spent doing occlusion tests for shadow rays and closest-point intersections, which are essential for propagating light across surfaces. The method is evaluated in terms of correctness, through comparisons against ground truth images, and performance, by determining how viable the pipeline is for interactive rendering. Performance is also compared with that of other spatial data structures such as the K-D tree. |
| Description: | B.Sc. (Hons)(Melit.) |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/106973 |
| Appears in Collections: | Dissertations - FacICT - 2022 Dissertations - FacICTCS - 2022 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 21BCS010 - Scerri Luke Bjorn.pdf Restricted Access | 2.19 MB | Adobe PDF | View/Open Request a copy |
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