3D single object tracking (SOT) in LiDAR point clouds is a critical task in computer vision and autonomous driving. Despite great success having been achieved, the inherent sparsity of point clouds introduces a dual-redundancy challenge that limits existing trackers: (1) vast spatial redundancy from background noise impairs accuracy, and (2) informational redundancy within the foreground hinders efficiency. To tackle these issues, we propose CompTrack, a novel end-to-end framework that systematically eliminates both forms of redundancy in point clouds. First, CompTrack incorporates a Spatial Foreground Predictor (SFP) module to filter out irrelevant background noise based on information entropy, addressing spatial redundancy. Subsequently, its core is an Information Bottleneck-guided Dynamic Token Compression (IB-DTC) module that eliminates the informational redundancy within the foreground. Theoretically grounded in low-rank approximation, this module leverages an online SVD analysis to adaptively compress the redundant foreground into a compact and highly informative set of proxy tokens. Extensive experiments on KITTI, nuScenes and Waymo datasets demonstrate that CompTrack achieves top-performing tracking performance with superior efficiency, running at a real-time 90 FPS on a single RTX 3090 GPU.

The complexity of unmanned system operating environments necessitates multi-sensor fusion for robust, long-term perception. Precise extrinsic calibration across heterogeneous sensors is fundamental to effective fusion. While data-driven calibration methods with artificial intelligence offer high accuracy and efficiency, their generalization to unseen scenarios and new sensor configurations remains limited. To address this, we propose CMIF-Calib, a novel LiDAR-Camera extrinsic calibration framework. Our approach employs an encoder-decoder network with cascaded multi-attention modules to learn reliable cross-modal correspondences between LiDAR point clouds and camera images, enabling indirect estimation of extrinsic parameters. By decoupling sensor intrinsics from the calibration network, CMIF-Calib supports simultaneous calibration of diverse sensor types (varying camera intrinsics/LiDAR channels) and multiple sensor combinations (including multi-camera and multi-LiDAR setups). Extensive experiments across diverse datasets and platforms demonstrate that CMIF-Calib achieves higher calibration accuracy and superior generalization compared to existing methods. The implementation of CMIF-Calib will be made publicly available upon publication at https://github.com/LvXudong-HIT/CMIF-Calib.

In this work, we address the inherent limitations of Neural Radiance Field (NeRF) in synthesizing novel viewpoints within dynamic environments, particularly those compromised by moving objects. Such scenarios frequently yield reconstructions of suboptimal quality, characterized by blurriness and the presence of artifacts, which significantly undermines the fidelity of synthetic scenes. This limitation significantly restricts the potential applications of NeRF in autonomous driving contexts, such as scene editing, high-precision map construction, and related functionalities. To overcome these challenges, we propose a novel NeRF-based approach tailored to address the complexities associated with moving objects in monocular driving scenarios. The proposed approach combines optical flow analysis and semantic information to precisely detect and localize moving objects. This was then followed by an inpainting technique that guides the NeRF reconstruction process, effectively mitigating the adverse impacts of dynamic elements within the scene. Our model is further enhanced by incorporating depth and semantic data to refine the training process. We validate the efficacy of our approach through comprehensive experimentation on both synthetic and real-world driving datasets, as well as on challenging self-recorded realistic driving scenes. Our method achieves a performance improvement of up to 13% compared to previous state-of-the-art methods. Additionally, we verify the efficacy of our approach through comprehensive ablation analyses. Both the quantitative and qualitative results demonstrate the superiority especially in dynamic driving scenes, advancing the potential applications in autonomous driving contexts. Our code and self-collected data are available at https://github.com/GandalfTGrey/Nerf-KBS.git .

With the frequent use of self-supervised monocular depth estimation in robotics and autonomous driving, the model’s efficiency is becoming increasingly important. Most current approaches apply much larger and more complex networks to improve the precision of depth estimation. Some researchers incorporated Transformer into self-supervised monocular depth estimation to achieve better performance. However, this method leads to high parameters and high computation. We present a fully convolutional depth estimation network using contextual feature fusion. Compared to UNet++ and HRNet, we use high-resolution and low-resolution features to reserve information on small targets and fast-moving objects instead of long-range fusion. We further promote depth estimation results employing lightweight channel attention based on convolution in the decoder stage. Our method reduces the parameters without sacrificing accuracy. Experiments on the KITTI benchmark show that our method can get better results than many large models, such as Monodepth2, with only 30% parameters. The source code is available at https://github.com/boyagesmile/DNA-Depth.

We proposed a multi-sensor fusion-based localization and scene reconstruction method for a complex dynamic scene. The multi-level fusion between multiple sensors was implemented by fusing data collected from different sensors in different system modules. In the front-end of the system, the camera and the LiDAR assisted each other. The LiDAR point clouds provided 3D information for the feature points in the image. The moving objects elimination method based on the image can remove the points on the moving objects in the LiDAR point clouds for localization accuracy improvement and static 3D scene reconstruction. To further improve the localization accuracy, a combination of visual loop closure detection and LiDAR loop closure detection was utilized to ensure the global consistency of scene reconstruction. At the system’s back-end, the observation model of different sensors was integrated to construct a multiple constraint factor graph with nonlinear optimization to obtain the optimal system states. Experimental results demonstrated that the proposed multi-sensor fusion-based localization and scene reconstruction algorithm could operate robustly in multiple complex dynamic scenes.