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 multi-frame 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 – 140× over Euler-Flow, reducing the runtime from a day to 10× minutes per sequence. Over the faster but low-quality baseline, NSFP, Floxels achieves a speedup of ∼14×.

The increasingly heterogeneous modern hardware landscape is forcing database vendors to rethink basic design decisions: With more and more architectures to support, the traditional approach of building on hand-tuned operators might simply become too costand labor-intensive. With this problem in mind, we introduced the notion of a hardware-oblivious database engine, which avoids devicespecific optimizations and targets multiple different hardware architectures from a single code-base. We demonstrated the feasibility of this concept through Ocelot, a prototypical hardware-oblivious database that uses OpenCL to provide operators that can run on multiple architectures. In this demonstration, we show how we modified Ocelot to support self-learning algorithm adaptivity: The ability to automatically learn which algorithms are optimal for a given operation on a given hardware architecture. We present how to specify operators that can be executed by multiple algorithms, provide details about the underlying learning and decision routines, and demonstrate how our system picks the optimal algorithm when running on systems with multiple devices, such as CPUs and graphics cards.