Estimating the 3D position and orientation of objects in the environment with
a single RGB camera is a critical and challenging task for low-cost urban
autonomous driving and mobile robots. Most of the existing algorithms are based
on the geometric constraints in 2D-3D correspondence, which stems from generic
6D object pose estimation. We first identify how the ground plane provides
additional clues in depth reasoning in 3D detection in driving scenes. Based on
this observation, we then improve the processing of 3D anchors and introduce a
novel neural network module to fully utilize such application-specific priors
in the framework of deep learning. Finally, we introduce an efficient neural
network embedded with the proposed module for 3D object detection. We further
verify the power of the proposed module with a neural network designed for
monocular depth prediction. The two proposed networks achieve state-of-the-art
performances on the KITTI 3D object detection and depth prediction benchmarks,
respectively. The code will be published in
this https URL

通过研究路面对 3D 检测提供的额外信息，提出了一种嵌入式神经网络，能够充分利用这些应用程序特定的先验知识，从而实现使用单个 RGB 摄像机确定环境中对象的位置和方向的三维目标检测。

自动驾驶用基于地面的单目三维物体检测

Ground-aware Monocular 3D Object Detection for Autonomous Driving

Bird's-eye-view (BEV) is a powerful and widely adopted representation for
road scenes that captures surrounding objects and their spatial locations,
along with overall context in the scene. In this work, we focus on bird's eye
semantic segmentation, a task that predicts pixel-wise semantic segmentation in
BEV from side RGB images. This task is made possible by simulators such as
Carla, which allow for cheap data collection, arbitrary camera placements, and
supervision in ways otherwise not possible in the real world. There are two
main challenges to this task: the view transformation from side view to bird's
eye view, as well as transfer learning to unseen domains. Existing work
transforms between views through fully connected layers and transfer learns via
GANs. This suffers from a lack of depth reasoning and performance degradation
across domains. Our novel 2-staged perception pipeline explicitly predicts
pixel depths and combines them with pixel semantics in an efficient manner,
allowing the model to leverage depth information to infer objects' spatial
locations in the BEV. In addition, we transfer learning by abstracting
high-level geometric features and predicting an intermediate representation
that is common across different domains. We publish a new dataset called
BEVSEG-Carla and show that our approach improves state-of-the-art by 24% mIoU
and performs well when transferred to a new domain.

本文介绍了使用车载摄像头拍摄的 RGB 图像进行鸟瞰视角像素级别的物体语义分割的方法，为了解决这个问题，提出了一种新的两阶段感知管道，它明确预测像素深度，并将它们与像素语义结合起来进行有效的推断，同时，使用抽象的高层几何特征进行转移学习，方法能够提高 24% 的 mIoU。