Title: | Reflection probe interpolation for fast and accurate rendering of reflective materials |
Authors: | Gojoković, Katarina Lesar, Žiga Marolt, Matija |
Citation: | Journal of WSCG. 2024, vol. 32, no. 1-2, p. 81-70. |
Issue Date: | 2024 |
Publisher: | Václav Skala - UNION Agency |
Document type: | článek article |
URI: | http://hdl.handle.net/11025/57345 |
ISSN: | 1213 – 6972 1213 – 6980 (CD-ROM) 1213 – 6964 (on-line) |
Keywords: | odrazové sondy;interpolace;konvoluční neuronové sítě |
Keywords in different language: | reflection probes;interpolation;convolutional neural networks |
Abstract in different language: | In this paper, we aim to improve rendering reflections using environment maps on moving reflective objects. Such scenarios require multiple reflection probes to be positioned at various locations in a scene. During rendering, the closest reflection probe is typically chosen as the environment map of a specific object, resulting in sharp transitions between the rendered reflections when the object moves around the scene. To solve this problem, we developed two convolutional neural networks that dynamically synthesize the best possible environment map at a given point in the scene. The first network generates an environment map from the coordinates of a given point through a decoder architecture. In the second approach, we triangulated the scene and captured environment maps at the triangle vertices – these represent reflection probes. The second network receives at the input three environment maps captured at the vertices of the triangle containing the query point, along with the distances between the query point and the vertices. Through an encoder-decoder architecture, the second network performs smart interpolation of the three environment maps. Both approaches are based on the phenomenon of overfitting, which made it necessary to train each network individually for specific scenes. Both networks are successful at predicting environment maps at arbitrary locations in the scene, even if these locations were not part of the training set. The accuracy of the predictions strongly depends on the complexity of the scene itself. |
Rights: | © Václav Skala - UNION Agency © Václav Skala - UNION Agency |
Appears in Collections: | Volume 32, number 1-2 (2024) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
B13-2024.pdf | Plný text | 6,92 MB | Adobe PDF | View/Open |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/57345
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.