The field of 3D perception and localization is rapidly advancing, driven by improvements in sensor technology and algorithmic innovations. A common theme among recent developments is the focus on enhancing accuracy, robustness, and efficiency in various applications, including LiDAR scanning, visual-inertial odometry, and image-to-point cloud registration. Notable advancements include the introduction of new datasets and benchmarks, such as Bench-RNR and MCOD, which evaluate the performance of different LiDAR scanning patterns and camouflaged object detection methods. Innovative approaches like Omni-LIVO and CrossI2P have also been proposed to enhance the accuracy and efficiency of visual-inertial-LiDAR odometry and image-to-point cloud registration. In the area of 3D surface reconstruction and floorplan analysis, researchers have made significant progress in developing more robust and accurate methods for handling incomplete and noisy data. Edge-centric formulations, orthogonal projections, and semantic-geometric fusion have been leveraged to improve the reconstruction of floorplans and surfaces from point cloud data. The field of 3D object detection has also seen substantial advancements, with a focus on leveraging radar-camera fusion to achieve robust and accurate detection in various environmental conditions. Novel architectures have been designed to exploit the advantages of radar point clouds, leading to significant improvements in detection accuracy and inference speed. Furthermore, the field of 3D perception and scene understanding is rapidly advancing, with a focus on developing efficient and accurate methods for tasks such as point cloud segmentation, 3D object detection, and instance segmentation. The application of pre-trained 2D models to 3D tasks has shown promising results, enabling fast and accurate predictions. Overall, these advancements demonstrate the rapid progress being made in the field of 3D perception and localization, with a focus on improving accuracy, robustness, and efficiency in various applications.