The field of 3D perception and autonomous navigation is rapidly advancing, with a focus on developing more accurate and efficient methods for understanding and interacting with complex environments. Recent research has highlighted the importance of bridging the modality gap between 2D and 3D tasks, and has introduced novel frameworks and architectures for achieving this goal. Notable developments include the use of abstract bounding boxes to encode geometric structure and physical kinematics, and the integration of 2D semantic cues with 3D geometric reasoning. Additionally, there has been significant progress in the development of autonomous underwater cognitive systems, which enable adaptive navigation in complex oceanic conditions. Other areas of research have focused on improving the accuracy and reliability of inertial navigation systems, and on developing more effective methods for place recognition and localization. Overall, these advances have the potential to enable more sophisticated and autonomous systems, with applications in areas such as robotics, embodied intelligence, and environmental monitoring. Noteworthy papers include: Abstract 3D Perception for Spatial Intelligence in Vision-Language Models, which introduces a simple yet effective framework for improving spatial intelligence in vision-language models. Task-Aware 3D Affordance Segmentation via 2D Guidance and Geometric Refinement, which presents a novel geometry-optimized framework for understanding 3D scene-level affordances. Autonomous Underwater Cognitive System for Adaptive Navigation, which integrates SLAM with a cognitive architecture to enable adaptive navigation in complex underwater environments.