Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
This provides a new benchmark for researchers in computer vision to evaluate motion and stereo estimation methods on high-detail data, though it is incremental as it builds upon existing benchmark concepts.
The authors introduced Spring, a large, high-resolution, computer-generated benchmark for scene flow, optical flow, and stereo to address the lack of detailed structures in existing datasets, with results showing that current methods struggle to estimate fine details, leaving significant room for improvement.
While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation methodology. Hence, we introduce Spring $-$ a large, high-resolution, high-detail, computer-generated benchmark for scene flow, optical flow, and stereo. Based on rendered scenes from the open-source Blender movie "Spring", it provides photo-realistic HD datasets with state-of-the-art visual effects and ground truth training data. Furthermore, we provide a website to upload, analyze and compare results. Using a novel evaluation methodology based on a super-resolved UHD ground truth, our Spring benchmark can assess the quality of fine structures and provides further detailed performance statistics on different image regions. Regarding the number of ground truth frames, Spring is 60$\times$ larger than the only scene flow benchmark, KITTI 2015, and 15$\times$ larger than the well-established MPI Sintel optical flow benchmark. Initial results for recent methods on our benchmark show that estimating fine details is indeed challenging, as their accuracy leaves significant room for improvement. The Spring benchmark and the corresponding datasets are available at http://spring-benchmark.org.