Incremental 3D Object

Incremental 3D object detection and recognition focuses on developing systems that can continuously learn to identify new 3D objects without forgetting previously learned ones, a crucial challenge for real-world applications like robotics and autonomous driving. Current research emphasizes mitigating "catastrophic forgetting" through techniques like knowledge distillation, prompt engineering, and attention mechanisms that selectively focus on relevant geometric features. These methods aim to improve the robustness and adaptability of 3D perception systems by enabling them to learn from a continuously evolving stream of data, thereby enhancing their performance in dynamic environments. This research area is significant for advancing the capabilities of AI systems that interact with the physical world.