Occupancy Network

Occupancy networks are a class of neural networks designed to represent 3D scenes as occupancy probabilities within a volumetric grid, enabling efficient scene understanding and reconstruction from various input modalities like images and depth maps. Current research focuses on improving the accuracy, efficiency, and reliability of these networks, exploring architectures like convolutional and transformer-based models, and addressing challenges such as limited data, computational cost, and handling uncertainty. These advancements have significant implications for applications ranging from autonomous driving (predicting drivable space and object locations) to medical imaging (localizing anatomical structures) and architectural design (generating 3D building models from sketches). The development of more efficient and robust occupancy networks is driving progress in several fields requiring accurate 3D scene representation.