DCVNet: Dilated Cost Volume Networks for Fast Optical Flow

Huaizu Jiang, Erik Learned-Miller

The cost volume, capturing the similarity of possible correspondences across two input images, is a key ingredient in state-of-the-art optical flow approaches. When sampling for correspondences to build the cost volume, a large neighborhood radius is required to deal with large displacements, introducing a significant computational burden. To address this, a sequential strategy is usually adopted, where correspondence sampling in a local neighborhood with a small radius suffices. However, such sequential approaches, instantiated by either a pyramid structure over a deep neural network's feature hierarchy or by a recurrent neural network, are slow due to the inherent need for sequential processing of cost volumes. In this paper, we propose dilated cost volumes to capture small and large displacements simultaneously, allowing optical flow estimation without the need for the sequential estimation strategy. To process the cost volume to get pixel-wise optical flow, existing approaches employ 2D or separable 4D convolutions, which we show either suffer from high GPU memory consumption, inferior accuracy, or large model size. Therefore, we propose using 3D convolutions for cost volume filtering to address these issues. By combining the dilated cost volumes and 3D convolutions, our proposed model DCVNet not only exhibits real-time inference (71 fps on a mid-end 1080ti GPU) but is also compact and obtains comparable accuracy to existing approaches.

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