Juan Raúl Padrón Griffe

Epale! I am a Marie Sklodowska-Curie fellow of the EU Project PRIME and a PhD candidate at the Graphics and Imaging Lab (Universidad de Zaragoza). My PhD thesis under the supervision of Prof. Adolfo Muñoz and Adrian Jarabo focuses on developing theory and methods for accurate and efficient rendering of complex volumetric appearances. Previously, I obtained my Bachelor degree in Computer Science (2015) at the Central University of Venezuela. Later, I received my Master degree in Informatics (2020) at the Technical University of Munich. During my Master studies I focused mostly on the Computer Graphics and Vision subjects, where I was fortunate enough to be advised by Prof. Matthias Niessner and Dr. Justus Thies at the Visual Computing lab to conduct my research on 3D Scanning and Neural Rendering. I am currently looking for a postdoctoral position.

I am a computer scientist enthusiastic about the intersection of realistic image synthesis, graphics-based vision and machine learning for the digital acquisition, representation and understanding of the visual world. I am currently interested in pushing the state of the art on physically-based rendering of complex multi-scale materials like biological tissues. In my research, I rely on powerful tools like Monte Carlo simulation and gradient-based optimization. In the long term, I believe the combination of powerful forward models (simulation algorithms) and inverse models (gradient-based models) could be impactful in other interesting domains too like computational biology.

Projects

Fractal Terrain Generation using Noise Synthesis

2015, May 24    

In the last decades, the advances in modelling virtual worlds have been impressive and notorious, from primitive results (Alien, 1979) to visually complex ones (CryEngine, 2009). However, the process is mostly manual, laborious, repetitive and costly especially for large scenes. For this reason, an alternative approach called procedural modelling, where the content is created via a procedure or program, is becoming more popular. An open research challenge is the automatic generation of terrains, especially if we consider the inmense variety of shapes and appearances.

For my Bachelor thesis, we develop a terrain generator based on an extension of the noise synthesis technique that includes transformations in order to improve significantly the capacity and power of the heightmap generation. The fractal generator is able to synthesize several types of terrains such as a glacier, mountain range or plateau in an efficient and extensive way by using different base functions (value noise, Perlin noise) and transformations (domain distortion and filters). Furthermore, we implement a realtime 3D Viewer using OpenGL shaders in order to explore the generated heightmaps, which include features like triplanar texture mapping, detail maps, sky domes, Phong illumination model and a navigation map. Finally, we carried out several experiments in order to study and evaluate the impact of the different algorithms, base functions and transformations.

Results:

Terrain Scene 1 Terrain Scene 2 Terrain Scene 3

Advisor: Hector Navarro Supervisor: Rhadamés Carmona

This work was heavily inspired in three sources:
Real-time editing, synthesis, and rendering of infinite landscapes on GPUs
Interactive GPU-based procedural heightfield brushes
Value Noise Derivatives

Document Presentation

Twitter
# Procedural Modeling # Terrain Generation # Fractals # Noise Synthesis