The field of computer vision is witnessing significant advancements in LiDAR-guided stereo and SLAM systems, with a focus on improving accuracy, efficiency, and robustness. Recent developments have led to the creation of novel methods that leverage sparse LiDAR data, surfel splatting, and Gaussian Splatting to achieve highly accurate geometric representations and tracking. These innovations have enabled the development of systems that can thrive in challenging environments, such as outdoor urban scenes, and can provide high-quality mapping and tracking performance. Notably, the use of adaptive surface rendering strategies and pixel-aware Gaussian initialization has improved the efficiency and accuracy of these systems. Overall, the field is moving towards more robust and efficient LiDAR-guided stereo and SLAM systems that can operate in a wide range of scenarios. Noteworthy papers include: Leveraging Sparse LiDAR for RAFT-Stereo, which proposes a novel pre-filling approach to improve LiDAR-guided stereo matching accuracy. $S^3$LAM, which introduces a surfel splatting SLAM system that achieves highly accurate geometric representations. GSFusion, which presents an online LiDAR-Inertial-Visual mapping system that ensures high-precision map consistency. Stereo 3D Gaussian Splatting SLAM for Outdoor Urban Scenes, which proposes the first binocular 3D Gaussian Splatting SLAM system designed for outdoor scenarios.