{"url":"/dataset/spring","name":"Spring","full_name":"Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo","description_markdown":"**Spring** is a large, high-resolution and 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.\r\n\r\nSource: [Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo](https://arxiv.org/pdf/2303.01943v1.pdf)\r\n\r\nImage Source: [https://arxiv.org/pdf/2303.01943v1.pdf](https://arxiv.org/pdf/2303.01943v1.pdf)","description_withheld":null,"homepage":"https://spring-benchmark.org/","introduced_date":"2023-03-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/spring-a-high-resolution-high-detail-dataset","title":"Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo","first_author":"Lukas Mehl","url":null},"license":{"name":"CC BY 4.0","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Optical Flow Estimation","url":"/task/optical-flow-estimation","datasets_with_task":"/datasets/task/optical-flow-estimation"},{"name":"Stereo Depth Estimation","url":"/task/stereo-depth-estimation","datasets_with_task":"/datasets/task/stereo-depth-estimation"},{"name":"Scene Flow Estimation","url":"/task/scene-flow-estimation","datasets_with_task":"/datasets/task/scene-flow-estimation"},{"name":"Stereo Disparity Estimation","url":"/task/stereo-disparity-estimation","datasets_with_task":"/datasets/task/stereo-disparity-estimation"}],"languages":[],"variants":["Spring"],"data_loaders":[{"repo":"https://github.com/cv-stuttgart/spring_utils","url":"https://github.com/cv-stuttgart/spring_utils","frameworks":["pytorch"]}],"num_papers_in_archive":29,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/optical-flow-estimation-on-spring","task":"Optical Flow Estimation","dataset_variant":"Spring","rows":11,"metrics":["1px total"],"first_row_in_archive_order":{"model":"MEMFOF","paper":"/paper/memfof-high-resolution-training-for-memory","metrics":{"1px total":"3.289"},"code_links":[{"title":"msu-video-group/memfof","url":"https://github.com/msu-video-group/memfof"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/scene-flow-estimation-on-spring","task":"Scene Flow Estimation","dataset_variant":"Spring","rows":6,"metrics":["1px total"],"first_row_in_archive_order":{"model":"M-FUSE (F)","paper":"/paper/m-fuse-multi-frame-fusion-for-scene-flow","metrics":{"1px total":"34.896"},"code_links":[{"title":"cv-stuttgart/m-fuse","url":"https://github.com/cv-stuttgart/m-fuse"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/stereo-depth-estimation-on-spring","task":"Stereo Depth Estimation","dataset_variant":"Spring","rows":4,"metrics":["1px total"],"first_row_in_archive_order":{"model":"ACVNet","paper":"/paper/acvnet-attention-concatenation-volume-for","metrics":{"1px total":"14.772"},"code_links":[{"title":"gangweix/acvnet","url":"https://github.com/gangweix/acvnet"},{"title":"ibaiGorordo/ONNX-ACVNet-Stereo-Depth-Estimation","url":"https://github.com/ibaiGorordo/ONNX-ACVNet-Stereo-Depth-Estimation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/memfof-high-resolution-training-for-memory","title":"MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation","date":"2025-06-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":9,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dpflow-adaptive-optical-flow-estimation-with-1","title":"DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework","date":"2025-03-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/recurrent-partial-kernel-network-for","title":"Recurrent Partial Kernel Network for Efficient Optical Flow Estimation","date":"2024-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/high-resolution-multi-scale-raft-robust","title":"High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)","date":"2022-10-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/m-fuse-multi-frame-fusion-for-scene-flow","title":"M-FUSE: Multi-frame Fusion for Scene Flow Estimation","date":"2022-07-12","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/flowformer-a-transformer-architecture-for","title":"FlowFormer: A Transformer Architecture for Optical Flow","date":"2022-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":1,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/acvnet-attention-concatenation-volume-for","title":"Attention Concatenation Volume for Accurate and Efficient Stereo Matching","date":"2022-03-04","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gmflow-learning-optical-flow-via-global","title":"GMFlow: Learning Optical Flow via Global Matching","date":"2021-11-26","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":3,"samples_unverified":16,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/camliflow-bidirectional-camera-lidar-fusion","title":"CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation","date":"2021-11-20","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/raft-stereo-multilevel-recurrent-field","title":"RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching","date":"2021-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-to-estimate-hidden-motions-with","title":"Learning to Estimate Hidden Motions with Global Motion Aggregation","date":"2021-04-06","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/raft-3d-scene-flow-using-rigid-motion","title":"RAFT-3D: Scene Flow using Rigid-Motion Embeddings","date":"2020-12-01","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-neural-architecture-search-for-1","title":"Hierarchical Neural Architecture Search for Deep Stereo Matching","date":"2020-10-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/raft-recurrent-all-pairs-field-transforms-for","title":"RAFT: Recurrent All-Pairs Field Transforms for Optical Flow","date":"2020-03-26","rows_on_this_dataset":1,"code_links":17,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":66,"samples_ran":40,"samples_unverified":26,"pointer_only_for_licence":17,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ga-net-guided-aggregation-net-for-end-to-end","title":"GA-Net: Guided Aggregation Net for End-to-end Stereo Matching","date":"2019-04-13","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pwc-net-cnns-for-optical-flow-using-pyramid","title":"PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume","date":"2017-09-07","rows_on_this_dataset":1,"code_links":21,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flownet-20-evolution-of-optical-flow","title":"FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks","date":"2016-12-06","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":2,"samples_unverified":19,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/optical-flow-estimation-using-a-spatial","title":"Optical Flow Estimation using a Spatial Pyramid Network","date":"2016-11-03","rows_on_this_dataset":1,"code_links":8,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":12,"samples_harvested":149,"samples_ran":69,"samples_unverified":80,"pointer_only_for_licence":26,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}