  Scene flow estimation is a crucial component in the development of autonomous
driving and 3D robotics, providing valuable information for environment
perception and navigation. Despite the advantages of learning-based scene flow
estimation techniques, their domain specificity and limited generalizability
across varied scenarios pose challenges. In contrast, non-learning
optimization-based methods, incorporating robust priors or regularization,
offer competitive scene flow estimation performance, require no training, and
show extensive applicability across datasets, but suffer from lengthy inference
times. In this paper, we present OptFlow, a fast optimization-based scene flow
estimation method. Without relying on learning or any labeled datasets, OptFlow
achieves state-of-the-art performance for scene flow estimation on popular
autonomous driving benchmarks. It integrates a local correlation weight matrix
for correspondence matching, an adaptive correspondence threshold limit for
nearest-neighbor search, and graph prior rigidity constraints, resulting in
expedited convergence and improved point correspondence identification.
Moreover, we demonstrate how integrating a point cloud registration function
within our objective function bolsters accuracy and differentiates between
static and dynamic points without relying on external odometry data.
Consequently, OptFlow outperforms the baseline graph-prior method by
approximately 20% and the Neural Scene Flow Prior method by 5%-7% in accuracy,
all while offering the fastest inference time among all non-learning scene flow
estimation methods.
