It is difficult to perform 3D reconstruction from on-vehicle gathered video
due to the large forward motion of the vehicle. Even object detection and human
sensing models perform significantly worse on onboard videos when compared to
standard benchmarks because objects often appear far away from the camera
compared to the standard object detection benchmarks, image quality is often
decreased by motion blur and occlusions occur often. This has led to the
popularisation of traffic data-specific benchmarks. Recently Light Detection
And Ranging (LiDAR) sensors have become popular to directly estimate depths
without the need to perform 3D reconstructions. However, LiDAR-based methods
still lack in articulated human detection at a distance when compared to
image-based methods. We hypothesize that benchmarks targeted at articulated
human sensing from LiDAR data could bring about increased research in human
sensing and prediction in traffic and could lead to improved traffic safety for
pedestrians.

从车辆收集的视频中进行三维重建较为困难，车辆的前向运动速度较快。与标准基准相比，对象经常出现在相机远离的地方，图像质量通常会因为运动模糊而降低，并且经常会发生遮挡。这导致了交通数据专门基准的普及。最近，光探测与测距（LiDAR）传感器因其直接估计深度而变得流行，无需进行三维重建。然而，与基于图像的方法相比，基于 LiDAR 的方法在远距离的关节人体检测方面仍然存在欠缺。我们假设针对 LiDAR 数据的关节人体感知基准可以促进交通中关于人体感知和预测的研究，并可能提高行人的交通安全。

一辆移动车辆上的语义化和关节化行人感知

Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle

We present deep neural network methodology to reconstruct the 3d pose and
shape of people, given an input RGB image. We rely on a recently introduced,
expressivefull body statistical 3d human model, GHUM, trained end-to-end, and
learn to reconstruct its pose and shape state in a self-supervised regime.
Central to our methodology, is a learning to learn and optimize approach,
referred to as HUmanNeural Descent (HUND), which avoids both second-order
differentiation when training the model parameters,and expensive state gradient
descent in order to accurately minimize a semantic differentiable rendering
loss at test time. Instead, we rely on novel recurrent stages to update the
pose and shape parameters such that not only losses are minimized effectively,
but the process is meta-regularized in order to ensure end-progress. HUND's
symmetry between training and testing makes it the first 3d human sensing
architecture to natively support different operating regimes including
self-supervised ones. In diverse tests, we show that HUND achieves very
competitive results in datasets like H3.6M and 3DPW, aswell as good quality 3d
reconstructions for complex imagery collected in-the-wild.

本文提出一种基于深度神经网络的人体三维姿态和形状重建方法，使用全身统计 3D 人体模型和学习技巧来优化模型并将其应用于自监督模式下的姿态和形状状态重建，并基于新颖的循环方式更新姿态和形状参数，从而实现了优化损失和度量反差的统一，测试结果表明其在 H3.6M 和 3DPW 等数据集上均获得了极具竞争力的结果。