Accurate depth estimation is crucial for 3D scene comprehension in robotics and autonomous vehicles. Fisheye cameras, known for their wide field of view, have inherent geometric benefits. However, their use in depth estimation is restricted by a scarcity of ground truth data and image distortions. We present FisheyeDepth, a self-supervised depth estimation model tailored for fisheye cameras. We incorporate a fisheye camera model into the projection and reprojection stages during training to handle image distortions, thereby improving depth estimation accuracy and training stability. Furthermore, we incorporate real-scale pose information into the geometric projection between consecutive frames, replacing the poses estimated by the conventional pose network. Essentially, this method offers the necessary physical depth for robotic tasks, and also streamlines the training and inference procedures. Additionally, we devise a multi-channel output strategy to improve robustness by adaptively fusing features at various scales, which reduces the noise from real pose data. We demonstrate the superior performance and robustness of our model in fisheye image depth estimation through evaluations on public datasets and real-world scenarios. The project website is available at: https://github.com/guoyangzhaolFisheyeDepth.

Traffic sign is a critical map feature for navigation and traffic control. Nevertheless, current methods for traffic sign recognition rely on traditional deep learning models, which typically suffer from significant performance degradation considering the variations in data distribution across different regions. In this paper, we propose TSCLIP, a robust fine-tuning approach with the contrastive language-image pre-training (CLIP) model for worldwide cross-regional traffic sign recognition. We first curate a cross-regional traffic sign benchmark dataset by combining data from ten different sources. Then, we propose a prompt engineering scheme tailored to the characteristics of traffic signs, which involves specific scene descriptions and corresponding rules to generate targeted text descriptions. During the TSCLIP fine-tuning process, we implement adaptive dynamic weight ensembling (ADWE) to seamlessly incorporate outcomes from each training iteration with the zero-shot CLIP model. This approach ensures that the model retains its ability to generalize while acquiring new knowledge about traffic signs. To the best knowledge of authors, TSCLIP is the first contrastive language-image model used for the worldwide cross-regional traffic sign recognition task. The project website is available at: https://github.com/guoyangzhao/TSCLIP.

Road curbs are considered as one of the crucial and ubiquitous traffic features, which are essential for ensuring the safety of autonomous vehicles. Current methods for detecting curbs primarily rely on camera imagery or LiDAR point clouds. Image-based methods are vulnerable to fluctuations in lighting conditions and exhibit poor robustness, while methods based on point clouds circumvent the issues associated with lighting variations. However, it is the typical case that significant processing delays are encountered due to the voluminous amount of 3D points contained in each frame of the point cloud data. Furthermore, the inherently unstructured characteristics of point clouds poses challenges for integrating the latest deep learning advancements into point cloud data applications. To address these issues, this work proposes an annotation-free curb detection method leveraging Altitude Difference Image (ADI) (as shown in Fig. 1), which effectively mitigates the aforementioned challenges. Given that methods based on deep learning generally demand extensive, manually annotated datasets, which are both expensive and labor-intensive to create, we present an Automatic Curb Annotator (ACA) module. This module utilizes a deterministic curb detection algorithm to automatically generate a vast quantity of training data. Consequently, it facilitates the training of the curb detection model without necessitating any manual annotation of data. Finally, by incorporating a post-processing module, we manage to achieve state-of-the-art results on the KITTI 3D curb dataset [1] with considerably reduced processing delays compared to existing methods, which underscores the effectiveness of our approach in curb detection tasks. Our code and data will be open-sourced at: https://sites.google.com/view/adi-curb-detection.

3D lane detection is essential in autonomous driving (AD) as it extracts structural and traffic information from the road in 3D space, aiding autonomous vehicles in logical, safe, and comfortable path planning and motion control. Given the cost of sensors and the advantages of visual data in color information, 3D lane detection based on monocular vision is an important research direction in the realm of AD that increasingly gains attention in both industry and academia. Nevertheless, recent advancements in visual perception seem inadequate for the development of fully reliable 3D lane detection algorithms, which also hampers the progress of vision-based fully autonomous vehicles. We believe that it still leaves an open and interesting problem for improvement in 3D lane detection algorithms for autonomous vehicles using visual sensors, and significant enhancements are essentially required. This review summarizes and analyzes the current state of achievements in the field of 3D lane detection research. It covers all current monocular-based 3D lane detection processes, discusses the performance of these cutting-edge algorithms, analyzes the time complexity of various algorithms, and highlights the main achievements and limitations of ongoing research efforts. The survey also includes a comprehensive discussion of available 3D lane detection datasets and the challenges that researchers encounter but have not yet resolved. Finally, our work outlines future research directions and invites researchers and practitioners to join this exciting field.

Curb detection is a crucial function in intelligent driving, essential for determining drivable areas on the road. However, the complexity of road environments makes curb detection challenging. This paper introduces CurbNet, a novel framework for curb detection utilizing point cloud segmentation. To address the lack of comprehensive curb datasets with 3D annotations, we have developed the 3D-Curb dataset based on SemanticKITTI, currently the largest and most diverse collection of curb point clouds. Recognizing that the primary characteristic of curbs is height variation, our approach leverages spatially rich 3D point clouds for training. To tackle the challenges posed by the uneven distribution of curb features on the xy-plane and their dependence on high-frequency features along the z-axis, we introduce the Multi-Scale and Channel Attention (MSCA) module, a customized solution designed to optimize detection performance. Additionally, we propose an adaptive weighted loss function group specifically formulated to counteract the imbalance in the distribution of curb point clouds relative to other categories. Extensive experiments conducted on 2 major datasets demonstrate that our method surpasses existing benchmarks set by leading curb detection and point cloud segmentation models. Through the post-processing refinement of the detection results, we have significantly reduced noise in curb detection, thereby improving precision by 4.5 points. Similarly, our tolerance experiments also achieve state-of-the-art results. Furthermore, real-world experiments and dataset analyses mutually validate each other, reinforcing CurbNet’s superior detection capability and robust generalizability. The project website is available at:https://github.com/guoyangzhao/CurbNet/.

Monocular 3D object detection is essential for autonomous driving. However, current monocular 3D detection algorithms rely on expensive 3D labels from LiDAR scans, making it difficult to use in new datasets and unfamiliar environments. This study explores training a monocular 3D object detection model using a mix of 3D and 2D datasets. The proposed framework includes a robust monocular 3D model that can adapt to different camera settings, a selective-training strategy to handle varying class annotations in datasets, and a pseudo 3D training method using 2D labels to improve detection ability in scenes with only 2D labels (as shown in Fig. 1). By utilizing this framework, we can train models on a combination of 3D and 2D datasets to improve generalization and performance on new datasets with only 2D labels. Extensive experiments on KITTI, nuScenes, ONCE, Cityscapes, and BDD100K datasets showcase the scalability of our proposed approach. Here is our project page: https://sites.google.com/view/fmaafmono3d.

Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale depth prediction network named FSNet. FSNet contains four important improvements over existing self-supervised models: (1) a multichannel output representation for stable training of depth prediction in driving scenarios, (2) an optical-flow-based mask designed for dynamic object removal, (3) a self-distillation training strategy to augment the training process, and (4) an optimization-based post-processing algorithm in test time, fusing the results from visual odometry. With this framework, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360 dataset and the nuScenes dataset demonstrate the potential of FSNet. More visualizations are presented in https://sites.google.com/view/fsnet/homeNote to Practitioners—This paper was motivated by the problem of unsupervised monocular depth for robotic deployment. We notice that PoseNet is not generalizable and by nature monodepth2 only predict depths up to a scale. We believe that we should not expect PoseNet, a ResNet on a concatenation of two images, to produce more reliable poses than the localization module in a robot. So we try our best to completely avoid using PoseNet. This creates much unstability in training, but we managed to fix it in FSNet with multichannel output and self-distillation. We also believe the network should try to directly predict accurate depth with a correct scale at any cases. So our method could produce meaningful results on static frames or scenes with little/no VO points (same as the network’s direct prediction). There are images without VO points in our multi-frame experiment, but our method is robust enough to fix this problem. In future research, we will include multi-frame depth predictions for more accurate depth prediction.

With the fast development of autonomous driving technologies, there is an increasing demand for high-definition (HD) maps, which provide reliable and robust prior information about the static part of the traffic environments. As one of the important elements in HD maps, road lane centerline is critical for downstream tasks, such as prediction and planning. Manually annotating centerlines for road lanes in HD maps is labor-intensive, expensive and inefficient, severely restricting the wide applications of autonomous driving systems. Previous work seldom explores the lane centerline detection problem due to the complicated topology and severe overlapping issues of lane centerlines. In this paper, we propose a novel method named CenterLineDet to detect lane centerlines for automatic HD map generation. Our CenterLineDet is trained by imitation learning and can effectively detect the graph of centerlines with vehicle-mounted sensors (i.e., six cameras and one LiDAR) through iterations. Due to the use of the DETR-like transformer network, CenterLineDet can handle complicated graph topology, such as lane intersections. The proposed approach is evaluated on the large-scale public dataset NuScenes. The superiority of our CenterLineDet is demonstrated by the comparative results. Our code, supplementary materials, and video demonstrations are available at https://tonyxuqaq.github.io/projects/CenterLineDet/.

3D object detection with only image inputs is an interesting and important problem in computer vision and autonomous driving. Nowadays, most existing monocular 3D object detection algorithms rely solely on the approximation power of convolutional neural networks to learn a mapping from pixels to 3D predictions without knowing the projection matrix of the camera. To endow the networks with camera projection knowledge, we propose the Star-Convolution module for application to image-based 3D detection. The introduced module increases the receptive field of the detector and embeds the camera's projection geometry inside the network while keeping the network end-to-end trainable. We test the module with different baselines in both monocular and stereo 3D object detection, and we achieve significant improvements on both tasks. The code will be published at https://github.com/Owen-Liuyuxuan/visualDet3D.

Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms. To find road network graphs, manual annotation is usually inefficient and labor-intensive. Automatically detecting road network graphs could alleviate this issue, but existing works still have some limitations. For example, segmentation-based approaches could not ensure satisfactory topology correctness, and graph-based approaches could not present precise enough detection results. To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this article. In view of that high-resolution aerial images could be easily accessed all over the world nowadays, we make use of aerial images in our approach. Taken as input an aerial image, our approach iteratively generates road network graphs vertex-by-vertex. Our approach can handle complicated intersection points with various numbers of incident road segments. We evaluate our approach on a publicly available dataset. The superiority of our approach is demonstrated through comparative experiments. Our work is accompanied by a demonstration video which is available athttps://tonyxuqaq.github.io/projects/RNGDet/.

Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D image-based detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://github.com/Owen-Liuyuxuan/visualDet3D.