Recent top-performing temporal 3D detectors based on Lidars have increasingly adopted region-based paradigms. They first generate coarse proposals, followed by encoding and fusing regional features. However, indiscriminate sampling and fusion often overlook the varying contributions of individual points and lead to exponentially increased complexity as the number of input frames grows. Moreover, arbitrary result-level concatenation limits the global information extraction. In this paper, we propose a Focal Token Acquring-and-Scaling Transformer (FASTer), which dynamically selects focal tokens and condenses token sequences in an adaptive and lightweight manner. Empha-sizing the contribution of individual tokens, we propose a simple but effective Adaptive Scaling mechanism to capture geometric contexts while sifting out focal points. Adaptively storing and processing only focal points in historical frames dramatically reduces the overall complexity. Furthermore, a novel Grouped Hierarchical Fusion strategy is proposed, progressively performing sequence scaling and Intra-Group Fusion operations to facilitate the exchange of global spatial and temporal information. Experiments on the Waymo Open Dataset demonstrate that our FASTer significantly outperforms other state-of-the-art detectors in both performance and efficiency while also exhibiting improved flexibility and robustness. The code is available at https://github.com/MSunDYY/FASTer.git.

Multimodal 3D occupancy prediction has garnered significant attention for its potential in autonomous driving. However, most existing approaches are single-modality: camera-based methods lack depth information, while LiDAR-based methods struggle with occlusions. Current lightweight methods primarily rely on the Lift-Splat-Shoot (LSS) pipeline, which suffers from inaccurate depth estimation and fails to fully exploit the geometric and semantic information of 3D LiDAR points. Therefore, we propose a novel multimodal occupancy prediction network called SDG-OCC, which incorporates a joint semantic and depth-guided view transformation coupled with a fusion-to-occupancy-driven active distillation. The enhanced view transformation constructs accurate depth distributions by integrating pixel semantics and co-point depth through diffusion and bilinear discretization. The fusion-to-occupancy-driven active distillation extracts rich semantic information from multimodal data and selectively transfers knowledge to image features based on LiDAR-identified regions. Finally, for optimal performance, we introduce SDG-Fusion, which uses fusion alone, and SDG-KL, which integrates both fusion and distillation for faster inference. Our method achieves state-of-the-art (SOTA) performance with real-time processing on the Occ3D-nuScenes dataset and shows comparable performance on the more challenging SurroundOcc-nuScenes dataset, demonstrating its effectiveness and robustness. The code will be released at https://github.com/DzpLab/SDGOCC.

In autonomous driving, the LiDAR-Camera system plays a crucial role in a vehicle’s perception of 3D environments. To effectively fuse information from both camera and LiDAR, extrinsic calibration is indispensable. Recently, some researchers have proposed deep learning-based methods that utilize convolutional networks to automatically extract features from LiDAR depth images and RGB images for calibration. However, these features do not sufficiently interact during feature matching, which limits the calibration accuracy. To this end, we introduce a novel extrinsic calibration network (HIFM-Net) in this paper. It establishes a comprehensive connection between camera and LiDAR features by calculating a globally-aware map-to-map cost volume and hierachical point-to-map cost volumes. The former is used to regress large extrinsic offsets. The latter is employed to iteratively fine-tune extrinsic parameters, while the rigidity of LiDAR points is considered in each iteration to enhance regression robustness. Extensive experiments on the KITTI-odometry dataset demonstrate the superior performance of our HIFMNet compared to other state-of-the-art learning-based methods.

Point clouds play a crucial role in robots and intelligent vehicles. Noise caused by fog droplets seriously degrades the quality of point clouds. Previous researches have shown that the extent of degradation is correlated with visibility. The fog attenuation coefficient is associated with visibility. In light of this background, this paper proposes a noise-distribution-based defogging method for point clouds. Our approach hinges on the estimation of the fog attenuation coefficient, facilitated by road-based prior knowledge. Subsequently, our method integrates the fog-induced noise distribution inferred from the LiDAR imaging model with the spatially non-uniform distribution of point clouds caused by LiDAR structure. The fused results are input to a statistical filter based on the relative sparsity of noise to achieve defogging. This paper is one of the early works focusing on point cloud defogging. Its core insight lies in the estimation of the attenuation coefficient and the employment of fog-induced noise distribution for defogging. Experiments demonstrate that our method can accurately mitigate the impact of fog and meanwhile enhance the performance of 3D object detection network.

Image and point cloud registration (2D-3D registration) is an essential prerequisite for multi-modal feature fusion. However, due to the significant feature difference of point cloud and image, it is challenging to establish 2D-3D correspondences. Targeting for the background of autonomous driving, we propose 2D-3D registration method with object-level correspondence (OL-Reg) in this paper. Object-level correspondence consists of object bounding box and object contour in 2D image and 3D space. The first step is to match 2D-3D objects. Due to sensor pose and field of view (FoV) difference, object shape and occlusion is different in image and point cloud, causing the difficulty of object matching. To solve this issue, we represent object as 3D bounding box, and design 2D-3D object matching with 3D box projection (Box-Proj) constraint. It aligns object 3D bounding box in image and point cloud. After that, the next step is to build 2D-3D correspondence from the matched objects. To extract correspondence from object with irregular shape, we notice the distance constraint of object surface and rays back-projected from object contour, and present projection based iterative closest point (Proj-ICP). Towards the stability of Proj-ICP, object-level regularization term is designed. Experiment is conducted in KITTI object and odometry dataset. With the pre-trained 3D object detector, results suggest that OL-Reg has the better performance than current approaches in tasks of re-localization and extrinsic calibration. And source code will be released soon1.

For 3D LIDAR object detection, data augmentation is an important module to make full use of precious annotated data. As a widely used data augmentation method, GT-aug effectively improves detection performance by inserting sampled groundtruths into LIDAR frames. However, they are often placed in unreasonable areas, leading to the loss of the semantic information between targets and backgrounds during training. To address this problem, we propose a context-aware data augmentation method (CA-aug), which ensures the proper placement of inserted objects by a simple strategy and produces realistic augmented scenes. CA-aug is lightweight and compatible with other augmentation methods. Experiments conducted on KITTI benckmark show that compared with the GT-aug and the similar method in LIDAR-aug (SOTA), it brings higher accuracy to the existing models especially for the detection of cyclists and perdestrians. We also present an in-depth study of augmentation strategies for the range-view-based (RV-based) models and demonstrate that CA-aug can fully exploit the potential of RV-based networks, boosting the moderate mAP of our test model by 8%.