3D Object Detection

3D object detection aims to accurately identify and locate objects within three-dimensional space, primarily using sensor data like LiDAR and cameras. Current research emphasizes improving accuracy and efficiency through advanced model architectures such as PointPillars, transformers, and Gaussian splatting, often incorporating multimodal fusion techniques and active learning strategies to reduce annotation costs. This field is crucial for autonomous driving, robotics, and augmented reality, with ongoing efforts focused on enhancing robustness, generalization across diverse datasets, and reducing computational demands for real-time applications.