The recent advancements in 3D perception and autonomous driving have seen a significant shift towards leveraging multi-modal data and zero-shot learning capabilities. Researchers are increasingly focusing on developing frameworks that can handle the complexities and variability of real-world scenarios without relying heavily on extensive labeled datasets. This trend is evident in the integration of vision foundation models with 3D representations, enabling more robust and scalable solutions for tasks such as 3D object segmentation and semantic mapping in off-road environments. Additionally, there is a growing emphasis on interactive and user-guided segmentation techniques that enhance the flexibility and usability of 3D perception systems. These developments not only push the boundaries of current technology but also pave the way for more versatile and adaptive applications in robotics and autonomous navigation.

Noteworthy contributions include a novel framework for zero-shot offboard panoptic perception in autonomous driving, which pioneers multi-modal integration for auto-labeling. Another significant advancement is the introduction of a scalable zero-shot 3D part segmentation framework that addresses the limitations of text-prompted methods, enhancing both scalability and flexibility. Lastly, an interactive segmentation method using 3D Gaussian Splatting demonstrates competitive performance without additional training, highlighting the potential of graph-based approaches in 3D scene analysis.