We present \textit{VoxelKP}, a novel fully sparse network architecture
tailored for human keypoint estimation in LiDAR data. The key challenge is that
objects are distributed sparsely in 3D space, while human keypoint detection
requires detailed local information wherever humans are present. We propose
four novel ideas in this paper. First, we propose sparse selective kernels to
capture multi-scale context. Second, we introduce sparse box-attention to focus
on learning spatial correlations between keypoints within each human instance.
Third, we incorporate a spatial encoding to leverage absolute 3D coordinates
when projecting 3D voxels to a 2D grid encoding a bird's eye view. Finally, we
propose hybrid feature learning to combine the processing of per-voxel features
with sparse convolution. We evaluate our method on the Waymo dataset and
achieve an improvement of $27\%$ on the MPJPE metric compared to the
state-of-the-art, \textit{HUM3DIL}, trained on the same data, and $12\%$
against the state-of-the-art, \textit{GC-KPL}, pretrained on a $25\times$
larger dataset. To the best of our knowledge, \textit{VoxelKP} is the first
single-staged, fully sparse network that is specifically designed for
addressing the challenging task of 3D keypoint estimation from LiDAR data,
achieving state-of-the-art performances. Our code is available at
https://github.com/shijianjian/VoxelKP.

VoxelKP 是第一个专为从 LiDAR 数据中进行 3D 关键点估计的单阶段完全稀疏网络，通过提出稀疏选择性卷积核、稀疏盒子注意力、空间编码和混合特征学习等四个创新方法，在 Waymo 数据集上实现了与当前最先进模型相比 27% 的 MPJPE 度量改进和 12% 的性能提升。

VoxelKP：基于体素的网络结构用于 LiDAR 数据中的人体关键点估计

VoxelKP: A Voxel-based Network Architecture for Human Keypoint  Estimation in LiDAR Data

In this paper, we tackle the challenging problem of 3D keypoint estimation of
general objects using a novel implicit representation. Previous works have
demonstrated promising results for keypoint prediction through direct
coordinate regression or heatmap-based inference. However, these methods are
commonly studied for specific subjects, such as human bodies and faces, which
possess fixed keypoint structures. They also suffer in several practical
scenarios where explicit or complete geometry is not given, including images
and partial point clouds. Inspired by the recent success of advanced implicit
representation in reconstruction tasks, we explore the idea of using an
implicit field to represent keypoints. Specifically, our key idea is employing
spheres to represent 3D keypoints, thereby enabling the learnability of the
corresponding signed distance field. Explicit keypoints can be extracted
subsequently by our algorithm based on the Hough transform. Quantitative and
qualitative evaluations also show the superiority of our representation in
terms of prediction accuracy.

本研究采用新颖的隐式表示方法解决了通用物体的 3D 关键点估计问题，通过使用球来表示 3D 关键点，以此实现对应的带符号距离场的可学习性，并基于 Hough 变换后提取出显式的关键点。实验结果表明，该表示方法在预测精度方面具有优越性。