DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow

Risheek Garrepalli, Jisoo Jeong, Rajeswaran C. Ravindran, Jamie Menjay Lin, Fatih Porikli; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 2220-2229

Abstract


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 & 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 realtime 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 on KITTI with manageable accuracy-performance tradeoffs.

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[bibtex]
@InProceedings{Garrepalli_2023_CVPR, author = {Garrepalli, Risheek and Jeong, Jisoo and Ravindran, Rajeswaran C. and Lin, Jamie Menjay and Porikli, Fatih}, title = {DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {2220-2229} }