Neural Shadow Ray Queries

Abstract

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.