构建可控基线的立体生成基准数据集，支持精确评估立体视觉算法性能。
StereoGenBench: A Synthetic Multi-Camera Benchmark for Stereo Generation under Controlled Baseline Regimes
- 基于 Unreal Engine 构建多相机合成场景，控制基线、内参、深度和运动参数
- 每场景生成最多15对校准视图，提供真实深度、内参、位姿等完整标注
- 适用于立体图像生成、视角合成等任务的公平对比，适合算法开发者与评测研究者
立体图像与视频生成、立体几何估计及条件控制的视图合成需要已知且可调控的成对数据，其中决定双目几何的变量——相机基线、内参、场景深度和相机运动——均需明确。现有资源仅提供部分变量，而当前广泛用于立体生成评估的数据集在我们所知范围内，并未提供同一场景下配对、校准的多基线右视图真值，且未同时包含联合记录的内参、密集度量深度和逐帧位姿。为此，我们提出 StereoGenBench，一个基于 Unreal Engine 的合成基准数据集，旨在实现基线范式敏感性和目标相机一致性在相同场景内容下的可测量性。每个场景通过刚性六相机横向阵列渲染，生成最多15对校准视图；相邻基线覆盖从瞳距到宽基线范围；焦距独立采样；每视图均提供 RGB、度量深度、内参、成对基线和逐帧位姿。数据划分包含窄基线与宽基线两种评估组别，以及一个仅用于训练的全对覆盖组。我们公开数据集、评估代码、参考结果、Croissant 元数据及生成代码/配置，支持兼容资产扩展。数据集可通过 https://huggingface.co/datasets/stereo-dataset/stereo-dataset 获取。
原文摘要 · Abstract (English)
Stereo image and video generation, stereo geometry estimation, and condition-controlled view synthesis require paired data in which the variables that determine binocular geometry -- camera baseline, intrinsics, scene depth, and camera motion -- are known and controllable. Existing stereo resources provide subsets of these variables, but resources commonly used for stereo generation evaluation do not, to our knowledge, provide scene-paired, calibrated multi-baseline right-view ground truth with jointly recorded intrinsics, dense metric depth, and per-frame poses in a single controlled source. We introduce StereoGenBench, a synthetic Unreal Engine benchmark designed to make baseline-regime sensitivity and target-camera consistency measurable under matched scene content. Each scene is rendered with a rigid six-camera lateral array, yielding up to 15 calibrated view pairs; adjacent baselines are sampled from inter-pupillary to wide-baseline regimes; focal length is sampled independently; and every view is released with RGB, metric depth, intrinsics, per-pair baselines, and per-frame poses. The splits include two evaluation families for narrow and wide baseline regimes and a train-only family for broader all-pairs coverage. We release the dataset, evaluation code, reference results, Croissant metadata, and generation code/configuration for extension with compatible assets. The dataset is available at https://huggingface.co/datasets/stereo-dataset/stereo-dataset
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