Scene Segmentation

Scene segmentation, the task of partitioning an image or 3D scene into meaningful regions, aims to provide a detailed understanding of the environment. Current research focuses on improving accuracy and efficiency, particularly in challenging conditions like low light or with limited annotations, employing diverse approaches such as neural fields, Gaussian splatting, and transformer-based architectures. These advancements are crucial for applications ranging from autonomous driving and robotics to medical image analysis and forest management, enabling more robust and reliable scene understanding in various domains. The field is also actively exploring unsupervised and weakly supervised learning methods to reduce reliance on large, manually labeled datasets.