Recently, several methods have been proposed to estimate 3D human pose from
multi-view images and achieved impressive performance on public datasets
collected in relatively easy scenarios. However, there are limited approaches
for extracting 3D human skeletons from multimodal inputs (e.g., RGB and
pointcloud) that can enhance the accuracy of predicting 3D poses in challenging
situations. We fill this gap by introducing a pipeline called PointVoxel that
fuses multi-view RGB and pointcloud inputs to obtain 3D human poses. We
demonstrate that volumetric representation is an effective architecture for
integrating these different modalities. Moreover, in order to overcome the
challenges of annotating 3D human pose labels in difficult scenarios, we
develop a synthetic dataset generator for pretraining and design an
unsupervised domain adaptation strategy so that we can obtain a well-trained 3D
human pose estimator without using any manual annotations. We evaluate our
approach on four datasets (two public datasets, one synthetic dataset, and one
challenging dataset named BasketBall collected by ourselves), showing promising
results. The code and dataset will be released soon.

提出了一种名为 PointVoxel 的流程，利用多视图 RGB 和 pointcloud 输入融合来获取 3D 人体姿势，并通过合成数据集生成器和无监督域自适应策略来克服在困难场景中标注 3D 人体姿势标签的挑战，展现了有希望的结果。

PointVoxel: 多视角多模态 3D 人体姿态估计的简单有效流程

PointVoxel: A Simple and Effective Pipeline for Multi-View Multi-Modal  3D Human Pose Estimation

LiDAR sensors play an important role in the perception stack of modern
autonomous driving systems. Adverse weather conditions such as rain, fog and
dust, as well as some (occasional) LiDAR hardware fault may cause the LiDAR to
produce pointcloud with abnormal patterns such as scattered noise points and
uncommon intensity values. In this paper, we propose a novel approach to detect
whether a LiDAR is generating anomalous pointcloud by analyzing the pointcloud
characteristics. Specifically, we develop a pointcloud quality metric based on
the LiDAR points' spatial and intensity distribution to characterize the noise
level of the pointcloud, which relies on pure mathematical analysis and does
not require any labeling or training as learning-based methods do. Therefore,
the method is scalable and can be quickly deployed either online to improve the
autonomy safety by monitoring anomalies in the LiDAR data or offline to perform
in-depth study of the LiDAR behavior over large amount of data. The proposed
approach is studied with extensive real public road data collected by LiDARs
with different scanning mechanisms and laser spectrums, and is proven to be
able to effectively handle various known and unknown sources of pointcloud
anomaly.

通过分析 LiDAR 点云特征，我们提出了一种新颖的方法来检测生成异常点云的 LiDAR，该方法基于纯数学分析开发了一个点云质量度量标准，无需标记或训练，可实现快速部署，这能够监测 LiDAR 数据中的异常以提高自主安全性，或离线进行大规模数据的深入研究。我们使用不同扫描机制和激光光谱采集的大量真实公路数据对该方法进行了研究，并证明其能够有效处理各种已知和未知的点云异常来源。