We follow [8] to train the depth estimation model and compute thedepth estimation error onpixelsassociated with ground truth LiDARpoints.

Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation.

Echocardiography is a key imaging modality for cardiac assessment but remains highly operator-dependent, and access to trained sonographers is limited in underserved settings. Teleoperated robotic echocardiography has been proposed as a solution; however, clinical studies report longer examination times than manual procedures, increasing diagnostic delays and operator workload. Automating non-expert tasks, such as automatically moving the probe to an ideal starting pose, offers a pathway to reduce this burden. Prior vision- and depth-based approaches to estimate an initial probe pose are sensitive to lighting, texture, and anatomical variability. We propose a robot-mounted 2D LiDAR-based approach that reconstructs the chest surface in 3D and estimates the initial probe pose automatically. To the best of our knowledge, this is the first demonstration of robot-mounted 2D LiDAR used for 3D reconstruction of a human body surface. Through plane-based extrinsic calibration, the transformation between the LiDAR and robot base frames was estimated with an overall root mean square (RMS) residual of 1.8 mm and rotational uncertainty below 0.2°. The chest front surface, reconstructed from two linear LiDAR sweeps, was aligned with non-rigid templates to identify an initial probe pose. A mannequin-based study assessing reconstruction accuracy showed mean surface errors of 2.78 +/- 0.21 mm. Human trials (N=5) evaluating the proposed approach found probe initial points typically 20-30 mm from the clinically defined initial point, while the variation across repeated trials on the same subject was less than 4 mm.

Accurate extrinsic calibration between LiDAR and camera sensors is important for reliable perception in autonomous systems. In this paper, we present a novel multi-objective optimization framework that jointly minimizes the geometric alignment error and computational cost associated with camera-LiDAR calibration. We optimize two objectives: (1) error between projected LiDAR points and ground-truth image edges, and (2) a composite metric for computational cost reflecting runtime and resource usage. Using the NSGA-II \cite{deb2002nsga2} evolutionary algorithm, we explore the parameter space defined by 6-DoF transformations and point sampling rates, yielding a well-characterized Pareto frontier that exposes trade-offs between calibration fidelity and resource efficiency. Evaluations are conducted on the KITTI dataset using its ground-truth extrinsic parameters for validation, with results verified through both multi-objective and constrained single-objective baselines. Compared to existing gradient-based and learned calibration methods, our approach demonstrates interpretable, tunable performance with lower deployment overhead. Pareto-optimal configurations are further analyzed for parameter sensitivity and innovation insights. A preference-based decision-making strategy selects solutions from the Pareto knee region to suit the constraints of the embedded system. The robustness of calibration is tested across variable edge-intensity weighting schemes, highlighting optimal balance points. Although real-time deployment on embedded platforms is deferred to future work, this framework establishes a scalable and transparent method for calibration under realistic misalignment and resource-limited conditions, critical for long-term autonomy, particularly in SAE L3+ vehicles receiving OTA updates.

Reconstructing and decomposing dynamic urban scenes is crucial for autonomous driving, urban planning, and scene editing. However, existing methods fail to perform instance-aware decomposition without manual annotations, which is crucial for instance-level scene editing.We propose UnIRe, a 3D Gaussian Splatting (3DGS) based approach that decomposes a scene into a static background and individual dynamic instances using only RGB images and LiDAR point clouds. At its core, we introduce 4D superpoints, a novel representation that clusters multi-frame LiDAR points in 4D space, enabling unsupervised instance separation based on spatiotemporal correlations. These 4D superpoints serve as the foundation for our decomposed 4D initialization, i.e., providing spatial and temporal initialization to train a dynamic 3DGS for arbitrary dynamic classes without requiring bounding boxes or object templates.Furthermore, we introduce a smoothness regularization strategy in both 2D and 3D space, further improving the temporal stability.Experiments on benchmark datasets show that our method outperforms existing methods in decomposed dynamic scene reconstruction while enabling accurate and flexible instance-level editing, making it a practical solution for real-world applications.

-- LiDAR-camera systems have become increasingly popular in robotics recently. A critical and initial step in integrating the LiDAR and camera data is the calibration of the LiDAR-camera system. Most existing calibration methods rely on auxiliary target objects, which often involve complex manual operations, whereas targetless methods have yet to achieve practical effectiveness. Recognizing that 2D Gaussian Splatting (2DGS) can reconstruct geometric information from camera image sequences, we propose a calibration method that estimates LiDAR-camera extrinsic parameters using geometric constraints. The proposed method begins by reconstructing colorless 2DGS using LiDAR point clouds. Subsequently, we update the colors of the Gaussian splats by minimizing the photometric loss. The extrinsic parameters are optimized during this process. Additionally, we address the limitations of the photometric loss by incorporating the reprojection and triangulation losses, thereby enhancing the calibration robustness and accuracy. I. INTRODUCTION LiDAR-camera fusion plays a critical role in autonomous driving and robotics. By integrating accurate depth measurements from LiDAR with dense optical scans provided by cameras, we can develop robust solutions for various tasks, including object detection [1], simultaneous localization and mapping (SLAM) [2], and 3D reconstruction [3].