![]() Specialization if combined with synthetic images. Trained either on annotated synthetic datasets or unlabeled videos, and better When trained with our data, state-of-the-art optical flow networksĪchieve superior generalization to unseen real data compared to the same models Optical flow field connecting each pixel in the input image to the one in the Then, we virtually move theĬamera in the reconstructed environment with known motion vectors and rotationĪngles, allowing us to synthesize both a novel view and the corresponding Plausible point cloud for the observed scene. Image, we use an off-the-shelf monocular depth estimation network to build a Specifically, we introduce aįramework to generate accurate ground-truth optical flow annotations quicklyĪnd in large amounts from any readily available single real picture. Synthetic datasets or unlabeled real videos. ![]() Networks, highlighting the limitations of existing sources such as labeled This paper deals with the scarcity of data for training optical flow
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