Monocular 3D lane detection is essential for a reliable autonomous driving system and has recently been rapidly developing. Existing popular methods mainly employ a predefined 3D anchor for lane detection based on front-viewed (FV) space, aiming to mitigate the effects of view transformations. However, the perspective geometric distortion between FV and 3D space in this FV-based approach introduces extremely dense anchor designs, which ultimately leads to confusing lane representations. In this paper, we introduce a novel prior-guided perspective on lane detection and propose an end-to-end framework named PVALane, which utilizes 2D prior knowledge to achieve precise and efficient 3D lane detection. Since 2D lane predictions can provide strong priors for lane existence, PVALane exploits FV features to generate sparse prior anchors with potential lanes in 2D space. These dynamic prior anchors help PVALane to achieve distinct lane representations and effectively improve the precision of PVALane due to the reduced lane search space. Additionally, by leveraging these prior anchors and representing lanes in both FV and bird-eye-viewed (BEV) spaces, we effectively align and merge semantic and geometric information from FV and BEV features. Extensive experiments conducted on the OpenLane and ONCE-3DLanes datasets demonstrate the superior performance of our method compared to existing state-of-the-art approaches and exhibit excellent robustness.

Monocular 3D lane detection has recently received increasing research attention in autonomous driving due to its application effectiveness and simplicity. However, depending solely on the limited semantic information from a single image makes current monocular detection methods unable to deal with complex scenarios, such as occluded, blurred, and unaligned scenes. In this study, we introduce an end-to-end framework named Bi2Lane which models temporal dependency in a continuous sequence. It recurrently utilizes detected lanes within historical frames as prior information to achieve robust lane detection. Additionally, Bi2Lane employs temporal reverse refinement together with temporal forward refinement to achieve bi-directional temporal refinement (BDTR) while maintaining a robust temporal dependency. For the refined features of different frames, we design a bi-level feature aggregation module (BLFA) to fuse them in both point-level and line-level manners, enabling a comprehensive feature representation to deal with complicated road scenes. Extensive experiments conducted on the OpenLane dataset demonstrate the superiority of Bi2Lane, achieving a notable F1 score of 63.8% using a simple ResNet50 backbone, surpassing the performance of existing state-of-the-art methods.

In the smart retail industry, the quality of image collection is known to heavily affect the final analysis results (like accuracy) of applications, such as commodity detection, identification, and stitching. In practice, images captured manually by a monocular camera like mobile phone contain many low-quality images caused by an irregular shoot step. After image collection, filtering out low-quality images is a key step to mitigate the aforementioned impacts. One of the most effective solutions is to filter images with huge off-angles in 3-D through the shelf pose estimation algorithm. However, most of the existing camera pose estimation algorithms are designed for natural scenes and are difficult to realize in the structured real target scenes (like shelf scenes). Meanwhile, due to the lack of shelf pose dataset in academia and industry, there is still no approach designed for the shelf pose estimation in the smart retail scenario. In this article, we try to regress the complete shelf pose within a single end-to-end network and propose a novel geometry-supervised pose network (GSPN), which supervises the shelf pose estimation by learning the intrinsically geometric properties of shelves. Furthermore, we introduce the first retail shelf pose dataset (RSPD), including 28 876 images selected from three different shelf categories and being annotated carefully, as well as a complete 3-D shelf posture. The whole networks can be trained end to end with the shelf images and well-annotated ground truth. Experiments result of five strategies show that GSPN achieves the state-of-the-art performance on RSPD.