VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data (2312.08871v1)

Abstract: 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 \url{https://github.com/shijianjian/VoxelKP}.

• The paper presents VoxelKP, a voxel-based network that leverages multi-scale feature aggregation and sparse operations for human keypoint estimation.
• It integrates sparse selective kernels, box-attention, BEV fusion, and hybrid feature learning to achieve a 27% improvement in MPJPE over prior methods.
• The results on the Waymo dataset highlight its potential in enhancing autonomous driving, robotics, and augmented reality applications.

An Overview of VoxelKP: A Voxel-Based Network Architecture for LiDAR Data Human Keypoint Estimation

This paper introduces VoxelKP, a voxel-based network architecture designed for human keypoint estimation in LiDAR data, addressing challenges associated with the sparse distribution of objects in 3D space and the dense requirements for human keypoints. Key components of the architecture include sparse selective kernels, sparse box-attention, spatial encoding, and hybrid feature learning. The network has been evaluated on the Waymo dataset and shows a marked improvement in performance, achieving state-of-the-art results without requiring extra training data.

The VoxelKP architecture developed in this paper leverages four innovative ideas to tackle the inherent challenges in human keypoint estimation from sparse LiDAR point clouds. The first component is the sparse selective kernels, which enhance spatial context by aggregating multi-scale 3D features. This approach selectively applies different kernel sizes to extract features around sparse voxel locations, significantly addressing the spatial relationships vital for keypoint detection. The second component, sparse box-attention, partitions sparse voxel space into non-overlapping boxes, accelerating the model's ability to capture localized dependencies within each partitioned region. Such fine-grained feature extraction is valuable in addressing densely clustered keypoints around human anatomy. Thirdly, the architecture incorporates spatially aware bird's eye view (BEV) fusion. By retaining 3D spatial information in the conversion of sparse voxel data to 2D representations, this method enhances the accuracy of keypoint estimation through innovative multi-scale fusion techniques. Finally, hybrid feature learning combines local fully connected layer (MLP) features with voxel-neighborhood sparse convolution features, boosting the detailed spatial resolution needed for accurate keypoint detection.

In assessing VoxelKP's performance, it's essential to consider numerical benchmarks that highlight its superior performance compared to existing methods. The architecture demonstrates a $27\%$ improvement in the mean per-joint position error (MPJPE) over the HUM3DIL model trained on the same dataset. Furthermore, it shows a $12\%$ improvement over the GC-KPL model, which was pretrained on a dataset $25\times$ larger than what VoxelKP uses. Such performance indicators underscore VoxelKP's robustness and capability in handling sparse data without relying on expansive dataset pre-training.

The implications of VoxelKP in advancing LiDAR-based human keypoint estimation are multifaceted. Practically, it can enhance various applications, including autonomous driving, robotics, and augmented reality, where accurate human pose recognition is crucial. Theoretically, the architecture sets a precedent for single-staged network designs in 3D data processing, emphasizing the utility of maintaining sparse representations throughout different architectural stages.

VoxelKP’s reliance on maintaining sparsity and its integration of voxel-based operators present intriguing possibilities for future work in AI beyond the current focus. As LiDAR technology continues to evolve in resolution and data richness, architectures like VoxelKP that balance the trade-offs between computational efficiency and data precision will be key in expanding the boundaries of 3D human pose estimation. Future developments could include enhancing temporal modeling capabilities to capture motion sequences or exploring extensions to a broader range of objects or environmental contexts.

Overall, VoxelKP demonstrates an innovative leap in voxel-based processing for human keypoint estimation, offering promising insights and directional shifts that align well with emerging trends and requirements in 3D data analytics and AI development.