High-Definition Stereo Video Dataset

Updated 11 December 2025

High-definition stereo video datasets are collections of synchronized left/right image sequences captured with calibrated dual-lens rigs, enabling precise 3D correspondence and depth estimation.
They use rigorous calibration protocols—including intrinsic/extrinsic calibration, rectification, and temporal synchronization—to ensure geometric and temporal fidelity.
These datasets support applications such as stereo matching, view synthesis, super-resolution, and SLAM, providing dense disparity and depth annotations for benchmarking.

A high-definition stereo video dataset is a collection of temporally coherent stereo image pairs (i.e., left/right video streams) captured, constructed, or curated with sufficient spatial and temporal resolution to support modern 3D vision research, including stereo matching, depth/disparity estimation, view synthesis, and algorithmic benchmarking. Such datasets are indispensable for evaluating and training learning-based methods in scenarios demanding precise correspondence, temporal stability, and geometric realism. Contemporary datasets span real-world, synthetic, and hybrid domains, with varying levels of calibration, ground-truth density, modality diversity, and licensing constraints.

1. Dataset Construction and Calibration Protocols

High-definition stereo video datasets are acquired using dual-lens rigs, synchronized multi-sensor arrays, or extracted from existing 3D movie content. Rigorous calibration protocols are fundamental for geometric fidelity:

Intrinsic Calibration: Estimation of focal length, principal point, and lens distortion for each camera, often via checkerboard-based approaches (e.g., Zhang's method).
Extrinsic Calibration: Left-to-right rotation $R$ and translation $T$ between sensors, typically computed once per session; ensures consistent epipolar geometry (Zhang et al., 16 Dec 2024).
Rectification: Computation of rectifying transforms aligns corresponding scanlines horizontally, enabling pixelwise disparity search.
Temporal Synchronization: Hardware triggering or internal clocks achieve sub-millisecond frame alignment for dynamic scenes, critical for accurate temporal correspondence, as in the Canon RF-S DUAL-lens, ZED, or RED Scarlet-X capture systems (Zhang et al., 16 Dec 2024, Choudhary et al., 2022, Banitalebi-Dehkordi, 2018).
Sensor and Baseline Choices: Stereo baselines typically span 8–120 mm, balancing depth sensitivity and eye comfort; some datasets emulate the human interpupillary distance (IPD ≈ 65 mm) for XR/VR compatibility (Xing et al., 10 Dec 2025).

2. Representative High-Definition Stereo Video Datasets

Empirical diversity, spatial fidelity, and annotation richness differentiate existing datasets. Table 1 presents core statistics:

Dataset	Seqs	Frames	Resolution/view	FPS	Baseline	Calib	GT Disp	License
StereoV1K	1,000	>500,000	1180 × 1180	50	≈60 mm	Full	Dense	CC BY-NC
StereoWorld-11M	142,520	≈11,000,000	1920 × 1080	24	55–75 mm (IPD)	Inherited	Dense	CC BY-NC-SA
WSVD	10,788	≈1,500,000	up to 1920 × 1080	30	Unknown	None	Semi	Research
SVD	310	≈300,000	1080p–2200²	30	19–63 mm	Full	Dense	CC BY
SHDR	11	6,600	1920 × 1080	30	80 mm	Partial	Absent*	Open/research
SVSR-Set	71	85,200	1920 × 1080	30	≈120 mm	Full	Sparse	Academic
Helvipad	29	39,553	1920 × 512 eqr	10	191 mm (vert.)	Full	Dense†	CC BY-4.0

*SHDR demonstrates depth map synthesis, but does not release framewise ground truth. †Helvipad combines partial LiDAR and label-dense depth-completion.

Key design parameters include side-by-side (SBS) encoding for consumer content, high frame rates for XR/AR (≥50 Hz), and ground-truth disparity generated either through classical stereo matchers (e.g., IGEV, SGBM) or via active sensors (LiDAR, ZED SDK).

3. Scene, Motion, and Illumination Diversity

Dataset content is engineered or curated to maximize coverage over key environmental and object parameters:

Environments: Indoor (offices, homes, retail), outdoor (streets, parks, facades), natural scenes, urban scenes, and synthetic environments (XR indoor modeling) (Zhang et al., 16 Dec 2024, Cheng et al., 2023, Choudhary et al., 2022).
Lighting and HDR: Datasets address both low-light (night, shadow), daylight, mixed, and high dynamic range (HDR) scenarios; some incorporate multi-exposure brackets and true HDR capture (18 F-stops, SHDR) (Choudhary et al., 2022, Banitalebi-Dehkordi, 2018).
Temporal and Motion Statistics: Camera can be static, stabilized, or freely moving, and scenes may contain objects in rigid, articulated, or deformable motion (people, cars, foliage). Scene descriptors and associated metadata are commonly provided (Zhang et al., 16 Dec 2024).
Non-Rigid Content: Internet-mined datasets (WSVD, YouTube-SBS, H2-Stereo) are rich in non-rigid targets such as people and crowds, a critical regime for depth learning (Wang et al., 2019, Shi et al., 30 Sep 2024).
Distribution and Sampling: Datasets such as StereoWorld-11M and YouTube-SBS sample from 3D movies and web uploads to achieve statistical coverage over genre, scene type, and lighting (Xing et al., 10 Dec 2025, Shi et al., 30 Sep 2024).

4. Ground-Truth Annotation, Data Formats, and Benchmarking

Datasets differ substantially in annotation scope, modality, and access protocols:

Image/Video Storage: Left/right views often stored as SBS video (e.g., MPEG-4/H.264 or .hevc) or separated PNG/EXR frames; frame-level access enables per-frame evaluation (Zhang et al., 16 Dec 2024, Izadimehr et al., 6 Jun 2025).
Disparity/Depth Maps: Ground-truth disparity available as 16-bit PNG or 32-bit float TIFF (StereoV1K), OpenEXR (StereoWorld-11M), or synthetic “z-buffer” (XR-Stereo). Computed via stereo matchers or derived from active sensors (ZED, LiDAR) (Zhang et al., 16 Dec 2024, Cheng et al., 2023, Choudhary et al., 2022, Zayene et al., 27 Nov 2024).
Metadata: Intrinsic/extrinsic calibration, exposure, GPS, lighting tags, per-frame pose (for SLAM/XR), and scene/class labels are provided via JSON or CSV sidecars (Zhang et al., 16 Dec 2024, Izadimehr et al., 6 Jun 2025).
Annotation Density: Label density varies: dense (full-resolution, synthetic or computed), semi-dense (via left-right consistency masking), or absent (YouTube aggregates, SHDR).
Benchmark Metrics: Standard metrics include PSNR, SSIM, LPIPS, MAE (StereoV1K reports PSNR=31.445 dB and SSIM=0.8522); disparity error (EPE, D1, D3, D5 on pixel thresholds), VMAF/SI/TI for streaming QoE tasks (Zhang et al., 16 Dec 2024, Izadimehr et al., 6 Jun 2025, Cheng et al., 2023).
Splits and Licensing: Training/test file hierarchies are consistently provided; licenses range from open (CC BY, CC BY-4.0 for research) to non-commercial (CC BY-NC, CC BY-NC-SA). Dataset size can exceed 500 GB (StereoV1K) and require institutional credentials for access (Zhang et al., 16 Dec 2024, Xing et al., 10 Dec 2025, Izadimehr et al., 6 Jun 2025).

5. Applications and Algorithmic Benchmarks

High-definition stereo video datasets address and enable research across multiple 3D vision domains:

Stereo and Disparity Estimation: Supervised pre-training, algorithmic benchmarking, and development of advanced cost aggregation/feature matching algorithms (Zhang et al., 16 Dec 2024, Cheng et al., 2023).
Monocular-to-Stereo and Novel View Synthesis: Training/evaluating monocular-to-stereo conversion frameworks, exploiting depth-warping, blend-inpainting, or geometry-aware video diffusion with explicit loss formulations (e.g., depth–disparity constraint $Z = fB/d$ ) (Zhang et al., 16 Dec 2024, Xing et al., 10 Dec 2025).
Super-Resolution and Quality Enhancement: Datasets such as SVSR-Set and SVD serve as targets for super-resolution, denoising, view enhancement, and QoE assessment tasks, with ground-truth for both LR↔HR and stereo pairs (Imani et al., 2022, Izadimehr et al., 6 Jun 2025).
SLAM, Visual Odometry, and XR Applications: Datasets with frame-level calibration and pose annotation (Synchronized Stereo/Plenoptic, XR-Stereo) provide standard testbeds for VO benchmarking using scale/rotation/translation/trajectory drift metrics (Zeller et al., 2018, Cheng et al., 2023).
Panoramic and Omnidirectional Stereo: Helvipad extends to equirectangular and top–bottom rigs, enabling full 360° stereo depth estimation under real-world conditions, benchmarked with adapted models (Zayene et al., 27 Nov 2024).
HDR and Multi-Exposure Fusion: SHDR and IIT-M datasets allow paper of 3D HDR coding, tone mapping, stereo depth under extreme dynamic ranges, and the effect of multi-exposure fusion (Choudhary et al., 2022, Banitalebi-Dehkordi, 2018).

6. Limitations and Open Directions

Despite advances in scale, fidelity, and diversity, high-definition stereo video datasets exhibit several limitations:

Scene Coverage and Annotation Completeness: Synthetic datasets may lack non-rigid motion, semantic diversity, or environmental variability; web-mined sets trade calibration and ground-truth density for breadth.
Temporal Consistency: Many datasets achieve high spatial resolution but may lack high frame rates or temporal length necessary for video-based applications and learning (Imani et al., 2022, Shi et al., 30 Sep 2024).
Calibration Consistency: In-the-wild sources (WSVD, YouTube-SBS) lack calibration and may have temporally inconsistent baselines, requiring normalization via special loss functions (e.g., NMG) (Wang et al., 2019).
Licensing: Commercial content (e.g., Blu-ray, YouTube) imposes significant limitations on use and redistribution (Xing et al., 10 Dec 2025, Shi et al., 30 Sep 2024).
Benchmark Gaps: Some datasets do not release quantitative benchmarks (SHDR), nor do they uniformly support HDR or multi-exposure analysis (Banitalebi-Dehkordi, 2018, Choudhary et al., 2022).

Continued progress is marked by the pursuit of IPD-aligned, temporally stable, annotated, and open-access datasets that combine synthetic scalability with real-world capture fidelity. Multi-modal synchronization (RGB, depth, events, LiDAR), panoramic rigs, and XR/AR integration remain key future axes.

7. Summary Table

Dataset	Size	Resolution	FPS	Calib	Annotations	License
StereoV1K	1,000 × 1,000	1180 × 1180	50	Full	Dense disp., scene tags, GPS	CC BY-NC
StereoWorld-11M	142K × 81	1920 × 1080	24/12	Inherited	Dense disp./depth	CC BY-NC-SA
WSVD	10,788	up to 1920×1080	30	None	Semi-dense disp., objects	Research
SVD	310	1080p–2200×2,200	30	Full	Disparity, depth, pose, SI/TI	CC BY
SHDR	11	1920 × 1080	30	Partial	HDR, depth bracket, SI/TI	Open
SVSR-Set	71	1920 × 1080	30	Full	(Some) Disparity, motion labels	Academic
Helvipad	29	1920 × 512 eqr	10	Full	Depth, disparity, completion	CC BY-4.0