Xin Zhao

Yining Shi, Kun Jiang, Xin Zhao et al. · tsinghua

LiDAR-based 3D object detection is a fundamental task in the field of autonomous driving. This paper explores the unique advantage of Frequency Modulated Continuous Wave (FMCW) LiDAR in autonomous perception. Given a single frame FMCW point cloud with radial velocity measurements, we expect that our object detector can detect the short-term future locations of objects using only the current frame sensor data and demonstrate a fast ability to respond to intermediate danger. To achieve this, we extend the standard object detection task to a novel task named predictive object detection (POD), which aims to predict the short-term future location and dimensions of objects based solely on current observations. Typically, a motion prediction task requires historical sensor information to process the temporal contexts of each object, while our detector's avoidance of multi-frame historical information enables a much faster response time to potential dangers. The core advantage of FMCW LiDAR lies in the radial velocity associated with every reflected point. We propose a novel POD framework, the core idea of which is to generate a virtual future point using a ray casting mechanism, create virtual two-frame point clouds with the current and virtual future frames, and encode these two-frame voxel features with a sparse 4D encoder. Subsequently, the 4D voxel features are separated by temporal indices and remapped into two Bird's Eye View (BEV) features: one decoded for standard current frame object detection and the other for future predictive object detection. Extensive experiments on our in-house dataset demonstrate the state-of-the-art standard and predictive detection performance of the proposed POD framework.

Jiaxing Hao, Yanxi Wang, Zhigang Chang et al.

Gait recognition is a remote biometric technology that utilizes the dynamic characteristics of human movement to identify individuals even under various extreme lighting conditions. Due to the limitation in spatial perception capability inherent in 2D gait representations, LiDAR can directly capture 3D gait features and represent them as point clouds, reducing environmental and lighting interference in recognition while significantly advancing privacy protection. For complex 3D representations, shallow networks fail to achieve accurate recognition, making vision Transformers the foremost prevalent method. However, the prevalence of dumb patches has limited the widespread use of Transformer architecture in gait recognition. This paper proposes a method named HorGait, which utilizes a hybrid model with a Transformer architecture for gait recognition on the planar projection of 3D point clouds from LiDAR. Specifically, it employs a hybrid model structure called LHM Block to achieve input adaptation, long-range, and high-order spatial interaction of the Transformer architecture. Additionally, it uses large convolutional kernel CNNs to segment the input representation, replacing attention windows to reduce dumb patches. We conducted extensive experiments, and the results show that HorGait achieves state-of-the-art performance among Transformer architecture methods on the SUSTech1K dataset, verifying that the hybrid model can complete the full Transformer process and perform better in point cloud planar projection. The outstanding performance of HorGait offers new insights for the future application of the Transformer architecture in gait recognition.