Spatial Constraint

Spatial constraint research focuses on efficiently and accurately incorporating spatial relationships between objects or entities into computational models and algorithms. Current work emphasizes developing novel architectures, such as factor graphs and foundation model-based approaches, to handle complex, higher-order constraints and improve the generalizability of solutions across diverse domains. This research is crucial for advancing applications ranging from robotics and AI-assisted design to image segmentation and autonomous driving, where accurately representing and satisfying spatial constraints is essential for reliable and safe operation. The development of efficient algorithms, particularly for large-scale problems, remains a key focus.