DiffSF: Diffusion Models for Scene Flow Estimation
CoRR(2024)
摘要
Scene flow estimation is an essential ingredient for a variety of real-world
applications, especially for autonomous agents, such as self-driving cars and
robots. While recent scene flow estimation approaches achieve a reasonable
accuracy, their applicability to real-world systems additionally benefits from
a reliability measure. Aiming at improving accuracy while additionally
providing an estimate for uncertainty, we propose DiffSF that combines
transformer-based scene flow estimation with denoising diffusion models. In the
diffusion process, the ground truth scene flow vector field is gradually
perturbed by adding Gaussian noise. In the reverse process, starting from
randomly sampled Gaussian noise, the scene flow vector field prediction is
recovered by conditioning on a source and a target point cloud. We show that
the diffusion process greatly increases the robustness of predictions compared
to prior approaches resulting in state-of-the-art performance on standard scene
flow estimation benchmarks. Moreover, by sampling multiple times with different
initial states, the denoising process predicts multiple hypotheses, which
enables measuring the output uncertainty, allowing our approach to detect a
majority of the inaccurate predictions.
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