Scalable Full Flow with Learned Binary Descriptors
This addresses computational bottlenecks in optical flow estimation for computer vision applications, representing an incremental improvement with specific efficiency gains.
The paper tackles the problem of large displacement optical flow by learning local matching costs with a CNN and imposing smoothness with a CRF, achieving scalable processing through min-projection and binary descriptors that reduce memory complexity from quadratic to linear.
We propose a method for large displacement optical flow in which local matching costs are learned by a convolutional neural network (CNN) and a smoothness prior is imposed by a conditional random field (CRF). We tackle the computation- and memory-intensive operations on the 4D cost volume by a min-projection which reduces memory complexity from quadratic to linear and binary descriptors for efficient matching. This enables evaluation of the cost on the fly and allows to perform learning and CRF inference on high resolution images without ever storing the 4D cost volume. To address the problem of learning binary descriptors we propose a new hybrid learning scheme. In contrast to current state of the art approaches for learning binary CNNs we can compute the exact non-zero gradient within our model. We compare several methods for training binary descriptors and show results on public available benchmarks.