Floxels: Fast Unsupervised Voxel Based Scene Flow Estimation
This work addresses the computational inefficiency of unsupervised optimization-based methods for scene flow estimation, which is crucial for robotic applications like dynamic object detection, though it is incremental in improving existing approaches.
The paper tackles scene flow estimation by introducing Floxels, a voxel grid-based model with a multiframe loss, which achieves comparable performance to top unsupervised methods on the Argoverse 2 benchmark while reducing runtime from a day to 10 minutes per sequence, offering a 60-140x speedup over EulerFlow.
Scene flow estimation is a foundational task for many robotic applications, including robust dynamic object detection, automatic labeling, and sensor synchronization. Two types of approaches to the problem have evolved: 1) Supervised and 2) optimization-based methods. Supervised methods are fast during inference and achieve high-quality results, however, they are limited by the need for large amounts of labeled training data and are susceptible to domain gaps. In contrast, unsupervised test-time optimization methods do not face the problem of domain gaps but usually suffer from substantial runtime, exhibit artifacts, or fail to converge to the right solution. In this work, we mitigate several limitations of existing optimization-based methods. To this end, we 1) introduce a simple voxel grid-based model that improves over the standard MLP-based formulation in multiple dimensions and 2) introduce a new multiframe loss formulation. 3) We combine both contributions in our new method, termed Floxels. On the Argoverse 2 benchmark, Floxels is surpassed only by EulerFlow among unsupervised methods while achieving comparable performance at a fraction of the computational cost. Floxels achieves a massive speedup of more than ~60 - 140x over EulerFlow, reducing the runtime from a day to 10 minutes per sequence. Over the faster but low-quality baseline, NSFP, Floxels achieves a speedup of ~14x.