  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.
