The estimation of implicit cross-frame correspondences and the high
computational cost have long been major challenges in video semantic
segmentation (VSS) for driving scenes. Prior works utilize keyframes, feature
propagation, or cross-frame attention to address these issues. By contrast, we
are the first to harness vanishing point (VP) priors for more effective
segmentation. Intuitively, objects near VPs (i.e., away from the vehicle) are
less discernible. Moreover, they tend to move radially away from the VP over
time in the usual case of a forward-facing camera, a straight road, and linear
forward motion of the vehicle. Our novel, efficient network for VSS, named
VPSeg, incorporates two modules that utilize exactly this pair of static and
dynamic VP priors: sparse-to-dense feature mining (DenseVP) and VP-guided
motion fusion (MotionVP). MotionVP employs VP-guided motion estimation to
establish explicit correspondences across frames and help attend to the most
relevant features from neighboring frames, while DenseVP enhances weak dynamic
features in distant regions around VPs. These modules operate within a
context-detail framework, which separates contextual features from
high-resolution local features at different input resolutions to reduce
computational costs. Contextual and local features are integrated through
contextualized motion attention (CMA) for the final prediction. Extensive
experiments on two popular driving segmentation benchmarks, Cityscapes and
ACDC, demonstrate that VPSeg outperforms previous SOTA methods, with only
modest computational overhead.

利用消失点先验知识的 VPSeg 模型在驾驶场景下进行视频语义分割，通过两个模块（DenseVP 和 MotionVP）利用静态和动态消失点先验知识，实现了更加有效的分割结果。这种模型在两个流行的驾驶分割基准数据集 Cityscapes 和 ACDC 上的实验证明了其在准确性方面的卓越表现，同时计算开销相对较小。

基于消失点引导的驾驶场景视频语义分割

Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes

Line segments are ubiquitous in our human-made world and are increasingly
used in vision tasks. They are complementary to feature points thanks to their
spatial extent and the structural information they provide. Traditional line
detectors based on the image gradient are extremely fast and accurate, but lack
robustness in noisy images and challenging conditions. Their learned
counterparts are more repeatable and can handle challenging images, but at the
cost of a lower accuracy and a bias towards wireframe lines. We propose to
combine traditional and learned approaches to get the best of both worlds: an
accurate and robust line detector that can be trained in the wild without
ground truth lines. Our new line segment detector, DeepLSD, processes images
with a deep network to generate a line attraction field, before converting it
to a surrogate image gradient magnitude and angle, which is then fed to any
existing handcrafted line detector. Additionally, we propose a new optimization
tool to refine line segments based on the attraction field and vanishing
points. This refinement improves the accuracy of current deep detectors by a
large margin. We demonstrate the performance of our method on low-level line
detection metrics, as well as on several downstream tasks using multiple
challenging datasets. The source code and models are available at
this https URL

DeepLSD 是一种结合了深度网络和传统手工设计的边缘检测器，能够在不需要真实边线作为训练数据的情况下进行自适应训练，并且通过优化吸引力场和消失点提高了检测的精度。

DeepLSD: 使用深度图像梯度进行线段检测和细化

DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

Monocular 3D object detection is an important task in autonomous driving. It
can be easily intractable where there exists ego-car pose change w.r.t. ground
plane. This is common due to the slight fluctuation of road smoothness and
slope. Due to the lack of insight in industrial application, existing methods
on open datasets neglect the camera pose information, which inevitably results
in the detector being susceptible to camera extrinsic parameters. The
perturbation of objects is very popular in most autonomous driving cases for
industrial products. To this end, we propose a novel method to capture camera
pose to formulate the detector free from extrinsic perturbation. Specifically,
the proposed framework predicts camera extrinsic parameters by detecting
vanishing point and horizon change. A converter is designed to rectify
perturbative features in the latent space. By doing so, our 3D detector works
independent of the extrinsic parameter variations and produces accurate results
in realistic cases, e.g., potholed and uneven roads, where almost all existing
monocular detectors fail to handle. Experiments demonstrate our method yields
the best performance compared with the other state-of-the-arts by a large
margin on both KITTI 3D and nuScenes datasets.

本篇论文提出了一种新颖的方法，通过检测消失点和地平线变化来预测摄像机的外部参数，从而解决了自动驾驶中相机外参引起的目标检测精度波动问题，并在 KITTI 3D 和 nuScenes 数据集上取得最好的性能表现。

单目三维物体检测：一种无外参方法

Monocular 3D Object Detection: An Extrinsic Parameter Free Approach

Inspired by the finding that vanishing point (road tangent) guides driver's
gaze, in our previous work we showed that vanishing point attracts gaze during
free viewing of natural scenes as well as in visual search (Borji et al.,
Journal of Vision 2016). We have also introduced improved saliency models using
vanishing point detectors (Feng et al., WACV 2016). Here, we aim to predict
vanishing points in naturalistic environments by training convolutional neural
networks in an end-to-end manner over a large set of road images downloaded
from Youtube with vanishing points annotated. Results demonstrate effectiveness
of our approach compared to classic approaches of vanishing point detection in
the literature.

通过训练卷积神经网络，我们针对自然场景中的路面图像进行了消失点预测，并与文献中经典的消失点检测方法进行了比较。结果显示，我们的方法能够更有效地预测消失点。