Improving the detection of distant 3d objects is an important yet challenging
task. For camera-based 3D perception, the annotation of 3d bounding relies
heavily on LiDAR for accurate depth information. As such, the distance of
annotation is often limited due to the sparsity of LiDAR points on distant
objects, which hampers the capability of existing detectors for long-range
scenarios. We address this challenge by considering only 2D box supervision for
distant objects since they are easy to annotate. We propose LR3D, a framework
that learns to recover the missing depth of distant objects. LR3D adopts an
implicit projection head to learn the generation of mapping between 2D boxes
and depth using the 3D supervision on close objects. This mapping allows the
depth estimation of distant objects conditioned on their 2D boxes, making
long-range 3D detection with 2D supervision feasible. Experiments show that
without distant 3D annotations, LR3D allows camera-based methods to detect
distant objects (over 200m) with comparable accuracy to full 3D supervision.
Our framework is general, and could widely benefit 3D detection methods to a
large extent.

使用仅有的 2D 监督信息，我们提出了 LR3D 框架，通过学习远距离物体的深度估计，并使得基于相机的方法能够在 200 米以上的距离实现与完整 3D 监督相当的准确性。

利用 2D 边界框监督改善远距离 3D 物体检测

Improving Distant 3D Object Detection Using 2D Box Supervision

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods
are limited due to expensive sensors and time-consuming computation.
Camera-based methods usually need to perform road segmentation and view
transformation separately, which often causes distortion and missing content.
To push the limits of the technology, we present a novel framework that
reconstructs a local map formed by road layout and vehicle occupancy in the
bird's-eye view given a front-view monocular image only. We propose a
front-to-top view projection (FTVP) module, which takes the constraint of cycle
consistency between views into account and makes full use of their correlation
to strengthen the view transformation and scene understanding. In addition, we
also apply multi-scale FTVP modules to propagate the rich spatial information
of low-level features to mitigate spatial deviation of the predicted object
location. Experiments on public benchmarks show that our method achieves the
state-of-the-art performance in the tasks of road layout estimation, vehicle
occupancy estimation, and multi-class semantic estimation. For multi-class
semantic estimation, in particular, our model outperforms all competitors by a
large margin. Furthermore, our model runs at 25 FPS on a single GPU, which is
efficient and applicable for real-time panorama HD map reconstruction.

提出基于前视单目图像的局部鸟瞰地图重建新框架，利用前向到俯视图像变换（FTVP）模块中的多尺度设计和周期性一致性约束，实现低成本高效的道路与汽车区域感知以及多类别场景理解。在公共基准测试中，该方法在道路布局估计，车辆占用估计和多类别语义估计任务中均达到了最先进的性能，并且在多类别语义估计中优于所有竞争对手。

通过前向上视图投影实现单目 BEV 道路场景感知

Monocular BEV Perception of Road Scenes via Front-to-Top View Projection

The use of cameras for vehicle speed measurement is much more cost effective
compared to other technologies such as inductive loops, radar or laser.
However, accurate speed measurement remains a challenge due to the inherent
limitations of cameras to provide accurate range estimates. In addition,
classical vision-based methods are very sensitive to extrinsic calibration
between the camera and the road. In this context, the use of data-driven
approaches appears as an interesting alternative. However, data collection
requires a complex and costly setup to record videos under real traffic
conditions from the camera synchronized with a high-precision speed sensor to
generate the ground truth speed values. It has recently been demonstrated that
the use of driving simulators (e.g., CARLA) can serve as a robust alternative
for generating large synthetic datasets to enable the application of deep
learning techniques for vehicle speed estimation for a single camera. In this
paper, we study the same problem using multiple cameras in different virtual
locations and with different extrinsic parameters. We address the question of
whether complex 3D-CNN architectures are capable of implicitly learning
view-invariant speeds using a single model, or whether view-specific models are
more appropriate. The results are very promising as they show that a single
model with data from multiple views reports even better accuracy than
camera-specific models, paving the way towards a view-invariant vehicle speed
measurement system.

使用多个摄像头和数据驱动的深度学习方法，能够更准确地测量车辆的速度，相比于基于单一摄像头的方法，该方案是更具前途的。