Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation

Understanding the motion states of the surrounding environment is critical for safe autonomous driving. These motion states can be accurately derived from scene flow, which captures the three-dimensional motion field of points. Existing LiDAR scene flow methods extract spatial features from each point cloud and then fuse them channel-wise, resulting in the implicit extraction of spatio-temporal features. Furthermore, they utilize 2D Bird's Eye View and process only two frames, missing crucial spatial information along the Z-axis and the broader temporal context, leading to suboptimal performance. To address these limitations, we propose Flow4D, which temporally fuses multiple point clouds after the 3D intra-voxel feature encoder, enabling more explicit extraction of spatio-temporal features through a 4D voxel network. However, while using 4D convolution improves performance, it significantly increases the computational load. For further efficiency, we introduce the Spatio-Temporal Decomposition Block (STDB), which combines 3D and 1D convolutions instead of using heavy 4D convolution. In addition, Flow4D further improves performance by using five frames to take advantage of richer temporal information. As a result, the proposed method achieves a 45.9% higher performance compared to the state-of-the-art while running in real-time, and won 1st place in the 2024 Argoverse 2 Scene Flow Challenge. The code is available at https://github.com/dgist-cvlab/Flow4D.

Domain Generalization in LiDAR Semantic Segmentation Leveraged by Density Discriminative Feature Embedding

While significant progress has been achieved in LiDAR-based perception, domain generalization continues to present challenges, often resulting in reduced performance when encountering unfamiliar datasets due to domain discrepancies. One of the primary hurdles stems from the variability of LiDAR sensors, leading to inconsistencies in point cloud density distribution. Such inconsistencies can undermine the effectiveness of perception models. We address this challenge by introducing a new approach that acknowledges a fundamental characteristic of LiDAR: the variation in point density due to the distance from the LiDAR to the scene, and the number of beams relative to the field of view. Understanding this, we view each LiDAR's point cloud at various distances as having distinct density distributions, which can be consistent across different LiDAR models. With this insight, we propose the Density Discriminative Feature Embedding (DDFE) module, crafted to specifically extract features related to density while ensuring domain invariance across different LiDAR sensors. In addition, we introduce a straightforward but effective density augmentation technique, designed to broaden the density spectrum and enhance the capabilities of the DDFE. The proposed DDFE stands out as a versatile and lightweight domain generalization module. It can be seamlessly integrated into various 3D backbone networks, consistently outperforming existing state-of-the-art domain generalization approaches. We commit to releasing the source code publicly to foster community collaboration and advancement.