Large Object

Research on large object processing focuses on overcoming challenges in accurately detecting, segmenting, and reconstructing large objects in various contexts, from satellite imagery to 3D scene understanding. Current efforts concentrate on improving model robustness to noise and size variations, often employing techniques like modified loss functions (e.g., Dice loss) and incorporating contextual information through methods such as attention mechanisms and graph neural networks. These advancements are crucial for improving the accuracy and efficiency of applications ranging from autonomous driving and environmental monitoring to industrial quality control and human-computer interaction.