An accurate understanding of a self-driving vehicle's surrounding environment
is crucial for its navigation system. To enhance the effectiveness of existing
algorithms and facilitate further research, it is essential to provide
comprehensive data to the routing system. Traditional approaches rely on
installing multiple sensors to simulate the environment, leading to high costs
and complexity. In this paper, we propose an alternative solution by generating
a top-down representation of the scene, enabling the extraction of distances
and directions of other cars relative to the ego vehicle. We introduce a new
synthesized dataset that offers extensive information about the ego vehicle and
its environment in each frame, providing valuable resources for similar
downstream tasks. Additionally, we present an architecture that transforms
perspective view RGB images into bird's-eye-view maps with segmented
surrounding vehicles. This approach offers an efficient and cost-effective
method for capturing crucial environmental information for self-driving cars.
Code and dataset are available at
this https URL

自动驾驶汽车的导航系统需要准确理解周围环境，本文提出了一个替代方法，通过生成场景的顶视图，提取其他车辆相对于自我车辆的距离和方向，同时通过将透视图 RGB 图像转换为鸟瞰地图的方法，有效捕捉了自动驾驶汽车所需的重要环境信息。

基于深度透视变换的鸟瞰图车辆定位

Deep Perspective Transformation Based Vehicle Localization on Bird's Eye  View

Recent research has demonstrated the ability to estimate gaze on mobile
devices by performing inference on the image from the phone's front-facing
camera, and without requiring specialized hardware. While this offers wide
potential applications such as in human-computer interaction, medical diagnosis
and accessibility (e.g., hands free gaze as input for patients with motor
disorders), current methods are limited as they rely on collecting data from
real users, which is a tedious and expensive process that is hard to scale
across devices. There have been some attempts to synthesize eye region data
using 3D models that can simulate various head poses and camera settings,
however these lack in realism.
In this paper, we improve upon a recently suggested method, and propose a
generative adversarial framework to generate a large dataset of high resolution
colorful images with high diversity (e.g., in subjects, head pose, camera
settings) and realism, while simultaneously preserving the accuracy of gaze
labels. The proposed approach operates on extended regions of the eye, and even
completes missing parts of the image. Using this rich synthesized dataset, and
without using any additional training data from real users, we demonstrate
improvements over state-of-the-art for estimating 2D gaze position on mobile
devices. We further demonstrate cross-device generalization of model
performance, as well as improved robustness to diverse head pose, blur and
distance.

本文提出了一种用生成对抗网络生成大型合成数据集来进行注视估计的方法，该方法在移动设备上取得了最新成果并具有跨设备泛化能力。