高保真动画片数据集，助力3D视觉与新视角生成研究
Charge: A Comprehensive Novel View Synthesis Benchmark and Dataset to Bind Them All
- 基于高质量动画电影构建多模态新视角合成数据集
- 涵盖密集、稀疏相机及单目视频三种场景设置
- 适合研究4D重建与视图合成的算法开发者
本文提出一个全新的新视角合成数据集，源自一部具有惊人真实感和复杂细节的高品质动画电影。该数据集包含多种动态场景，具备精细纹理、光照与运动信息，适用于训练和评估前沿的4D场景重建与新视角生成模型。除高保真RGB图像外，还提供深度、表面法向、物体分割和光流等多种互补模态，以深入理解场景几何与运动。数据集分为三个基准测试场景：密集多视角相机设置、稀疏相机布局以及单目视频序列，支持在不同数据稀疏度下广泛实验与对比。凭借其视觉丰富性、高质量标注和多样化的实验设置，该数据集为推动视图合成与三维视觉边界提供了独特资源。
原文摘要 · Abstract (English)
This paper presents a new dataset for Novel View Synthesis, generated from a high-quality, animated film with stunning realism and intricate detail. Our dataset captures a variety of dynamic scenes, complete with detailed textures, lighting, and motion, making it ideal for training and evaluating cutting-edge 4D scene reconstruction and novel view generation models. In addition to high-fidelity RGB images, we provide multiple complementary modalities, including depth, surface normals, object segmentation and optical flow, enabling a deeper understanding of scene geometry and motion. The dataset is organised into three distinct benchmarking scenarios: a dense multi-view camera setup, a sparse camera arrangement, and monocular video sequences, enabling a wide range of experimentation and comparison across varying levels of data sparsity. With its combination of visual richness, high-quality annotations, and diverse experimental setups, this dataset offers a unique resource for pushing the boundaries of view synthesis and 3D vision.
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