  Recent advancements in neural network-based optical flow estimation often
come with prohibitively high computational and memory requirements, presenting
challenges in their model adaptation for mobile and low-power use cases. In
this paper, we introduce a lightweight low-latency and memory-efficient model,
Dynamic Iterative Field Transforms (DIFT), for optical flow estimation feasible
for edge applications such as mobile, XR, micro UAVs, robotics and cameras.
DIFT follows an iterative refinement framework leveraging variable resolution
of cost volumes for correspondence estimation. We propose a memory efficient
solution for cost volume processing to reduce peak memory. Also, we present a
novel dynamic coarse-to-fine cost volume processing during various stages of
refinement to avoid multiple levels of cost volumes. We demonstrate first
real-time cost-volume based optical flow DL architecture on Snapdragon 8 Gen 1
HTP efficient mobile AI accelerator with 32 inf/sec and 5.89 EPE (endpoint
error) on KITTI with manageable accuracy-performance tradeoffs.
