Rendering shadows remains a fundamental problem in computer graphics, with existing approaches balancing performance against visual quality. Traditional shadow mapping techniques are efficient but suffer from aliasing artifacts due to its discretized nature. On the other hand, ray tracing can produce high quality shadow without discretization but is often too demanding for consumer hardware.

This thesis proposes a neural shadow representation that models a continuous mapping from light rays to occluder depth. Instead of relying on discretized buffers, we represent each object's shadow with a fully connected neural network. Given a ray origin and direction, the network predicts the depth at which the ray intersects geometry. This allows for depth tests similar to shadow mapping while supporting continuous input. The model is trained using ray-traced supervision with a dead-zone loss function that encourages the model to output depth corresponding to a position inside the occluder's geometry. It does not rely on discretized shadow maps and supports fully dynamic scenes, point lights, and directional lights.

Our results show that this method can achieve visual quality comparable to medium-resolution shadow maps while eliminating aliasing. The proposed method also has a unique ability to overfit to restricted light configurations. In scenes such as outdoor scenes where light movement is limited, the model's capacity is concentrated on relevant directions, allowing it to capture finer geometric details. While limitations remain regarding inference speed and high-frequency geometry, the proposed method demonstrates unique strengths that open up new trade-offs, particularly in scenarios with partially restrained lighting configurations and large scale but low-frequency geometry such as landscapes. ... Read more

Wildfires are increasing in both frequency and severity due to climate change, which increases the need for accurate and scalable fire modelling techniques. While existing simulation methods range from statistical models to physics-based approaches, most high-fidelity models rely on idealized, synthetic environments with complete scene knowledge. This limits their applicability to real-world forests, as well as evaluation opportunities.

This thesis addresses this gap by investigating wildfire simulation directly on real-world scene reconstructions using 3D Gaussian Splatting (3DGS). On the Feature Splatting framework, a complete pipeline is developed that reconstructs forest scenes from aerial imagery and augments them with semantic and material information.

A preprocessing framework is introduced to refine these reconstructions. It includes noise filtering, semantic classification, procedural stem insertion, and material column completion. On this representation, a physics-based, particle-driven fire simulation model is implemented, which models heat transfer, ignition, combustion, and fire spread directly on Gaussian primitives. The system is integrated into the Nerfstudio framework.

The evaluation demonstrates that the proposed simulation reproduces several characteristic wildfire behaviours reported in the literature. Fire spread increases with vegetation density and is strongly influenced by wind direction and speed. Slope experiments confirm that fire propagates faster uphill than downhill, with an accelerating increase in spread rate for steeper upward slopes. Wind-driven behaviour is qualitatively realistic, including faster burnout under stronger wind conditions. The simulation is stable across repeated runs and performs consistently on synthetic scenes.

In addition to standard evaluations, this thesis also introduces novel testing scenarios, such as forest obstruction experiments and burnt mass estimation. Results indicate that wind influence is slightly overestimated, particularly in firebreak scenarios where residual flammable material enables fire to cross barriers at lower wind speeds than expected. Burnt mass estimation reveals that the current model underestimates total biomass loss. Further calibration of material properties and combustion parameters is required.

Finally, the simulation is applied to real-world drone data. The system is capable of running wildfire simulations on reconstructed forest scenes, but challenges remain, such as noise in the data, limitations in semantic classification, and scalability issues, as these scenes are significantly larger

Overall, this work presents a novel and robust pipeline for wildfire simulation on Gaussian Splatting-based scene representations. ... Read more