ChessRender360: High-Fidelity Rendered Chess Dataset with Multi-Modal Annotations

ChessRender360 is a synthetically crafted dataset featuring 10,000 rendered chess positions. Designed for computer vision and machine learning research, this dataset provides a rich collection of RGB images, depth maps, instance masks, and semantic segmentation masks for each chess piece and board element. Each chess position is rendered in high resolution (2000x2000 pixels), capturing the intricate details of the board and pieces from various angles. The dataset includes: RGB Images: High-quality rendered images of chess positions, showcasing a diverse range of board configurations. Depth Maps: Accurate depth representations of the scene, capturing depth in the range of 20 cm to 120 cm. In the depth maps, black corresponds to a depth of 20 cm, and white corresponds to 120 cm, providing spatial information for each position. Instance Masks: Unique instance masks for each chess piece, enabling precise identification and localization. Semantic Segmentation Masks: Segmentation masks that differentiate between piece types and board elements, with distinct hue values assigned to each type. Bounding Boxes: Each sample has an annotation .json file containing bounding boxes for each piece. Board Corners: Same annotation .json contains positions of corners of the board in order: white left, white right, black left, black right. FENs: A CSV file containing the FEN (Forsyth-Edwards Notation) for each chess position in the dataset, listed in order. This allows users to easily recognize and replicate the exact board position from any image. Bounding Box Generation: The dataset does not include predefined bounding boxes, but they can be easily generated from the provided semantic and instance masks. This allows for flexible bounding box creation tailored to specific research needs. Rendering Details: 3D Models and Materials: The dataset uses a consistent set of 3D models for all chess pieces, with three different material/color schemes applied across the dataset, along with random perturbations in material brightness, contrast and saturation to introduce visual variety. Camera Angles: Camera angles are randomly selected, with yaw ranging from 0 to 360 degrees and pitch between 30 to 80 degrees, providing diverse perspectives of the chess positions. Background Variability: The chessboard is randomly placed on different types of tables, with the floor material randomly sampled to create a variety of backgrounds. Lighting: Lighting conditions are randomly generated, adding further diversity and realism to the rendered scenes. Augmentation Potential: The instance and semantic masks can be used to further augment the dataset. Researchers can selectively modify specific parts of the images—such as the board, background, or individual pieces—enabling the creation of new variations and enhancing the dataset's utility for model training and testing. Color Mapping: The semantic masks are color-coded using a hue-based system, where the board frame, squares, and each piece type are assigned specific hues. Instances of the same piece type are differentiated by varying the value component, with saturation consistently set to 1. A detailed color_mapping.json file is included, providing a comprehensive guide to interpreting the masks. Applications: ChessRender360 is ideal for tasks such as object detection, instance segmentation, depth estimation, and scene understanding in synthetic environments. Researchers and developers can leverage this dataset for training and evaluating models in computer vision, robotics, and artificial intelligence. Dataset Highlights: 10,000 uniquely rendered chess positions High-resolution images (2000x2000 pixels) with diverse visual characteristics Comprehensive annotations with RGB, depth (20 cm to 120 cm), instance, and semantic maps Side identification map for distinguishing white and black sides of the board FEN notation CSV file for easy position recognition and replication Variety introduced through different material schemes and lighting setups Randomized camera angles for enhanced perspective diversity Potential for further augmentation by modifying specific image components Detailed color mapping for easy interpretation of segmentation masks Suitable for a wide range of computer vision and AI applications ChessRender360 offers a rich and versatile dataset for advancing research and development in the field of computer vision, providing a synthetic yet highly realistic environment for model training and testing. For any questions, feedback, or collaboration opportunities, or if you are interested in custom artificial datasets, please contact me: Name: Marko Kojić LinkedIn: https://www.linkedin.com/in/mmkoya I welcome inquiries from researchers, developers, and organizations interested in utilizing or collaborating on artificial datasets.ChessRender360是一个人工合成构建的数据集，包含10000个渲染生成的国际象棋棋局。该数据集专为计算机视觉与机器学习研究设计，为每一个国际象棋棋子与棋盘元素提供了丰富的RGB图像、深度图（Depth Maps）、实例掩码（Instance Masks）以及语义分割掩码（Semantic Segmentation Masks）资源。 每个棋局均以2000×2000像素的高分辨率渲染，从多角度捕捉棋盘与棋子的精细细节。数据集包含以下内容： RGB图像：高质量的国际象棋棋局渲染图像，涵盖多样化的棋盘布局配置。 深度图（Depth Maps）：场景的精准深度表征，深度取值范围为20厘米至120厘米。在深度图中，黑色对应20厘米深度，白色对应120厘米深度，可为每个棋局提供空间信息。 实例掩码（Instance Masks）：为每一个国际象棋棋子生成的唯一实例掩码，可实现精确的识别与定位。 语义分割掩码（Semantic Segmentation Masks）：用于区分棋子类型与棋盘元素的分割掩码，为每一类对象分配了独特的色调值。 边界框（Bounding Boxes）：每个样本均附带一个.json注释文件，其中包含了每个棋子的边界框标注信息。 棋盘角点（Board Corners）：同一.json注释文件中还包含棋盘四个角点的坐标，顺序为：白方左侧、白方右侧、黑方左侧、黑方右侧。 FEN标注（Forsyth-Edwards Notation）：包含数据集内所有棋局的FEN标注的CSV文件，按顺序排列。用户可借此轻松识别并复现任意图像对应的精确棋盘布局。 边界框生成： 本数据集未预先提供边界框，但可通过已有的语义掩码与实例掩码轻松生成，从而能够根据具体研究需求灵活定制边界框。 渲染细节： 3D模型与材质：数据集为所有国际象棋棋子采用了统一的3D模型，并在全数据集范围内应用了三种不同的材质/配色方案，同时通过随机调整材质的亮度、对比度与饱和度来引入视觉多样性。 拍摄角度：相机角度随机选取，偏航角（yaw）范围为0至360度，俯仰角（pitch）范围为30至80度，可为棋局提供多样化的视角。 背景多样性：棋盘随机放置于不同类型的桌面上，同时随机采样地板材质以构建多样的背景环境。 光照条件：光照设置随机生成，进一步提升渲染场景的多样性与真实感。 数据增强潜力： 可利用实例掩码与语义掩码对数据集进行进一步增强。研究人员可选择性修改图像的特定区域——如棋盘、背景或单个棋子——从而生成新的样本变体，提升该数据集在模型训练与测试中的实用性。 颜色映射： 语义掩码采用基于色调的颜色编码系统，棋盘边框、棋盘格以及每一类棋子均被分配了特定的色调。同一棋子类型的不同实例通过调整明度分量进行区分，饱和度统一设置为1。数据集附带详细的color_mapping.json文件，可为掩码解读提供全面的参考指南。 应用场景： ChessRender360非常适用于合成环境下的目标检测、实例分割、深度估计以及场景理解等任务。研究人员与开发者可借助该数据集训练并评估计算机视觉、机器人学与人工智能领域的相关模型。 数据集亮点： 1. 包含10000个独立渲染的国际象棋棋局 2. 2000×2000像素的高分辨率图像，具备多样化的视觉特征 3. 涵盖RGB图像、深度（20厘米至120厘米）数据、实例掩码与语义掩码的完整标注体系 4. 支持区分棋盘白方与黑方区域的侧边识别映射图 5. 附带FEN标注CSV文件，便于快速识别与复现棋盘布局 6. 通过多样化的材质方案与光照设置引入视觉多样性 7. 随机化的相机角度，大幅提升视角多样性 8. 支持通过修改图像特定组件进行进一步的数据增强 9. 提供详细的颜色映射规则，便于快速解读分割掩码 10. 适用于广泛的计算机视觉与人工智能应用场景 ChessRender360是一款丰富且通用的数据集，可为计算机视觉领域的研究与开发提供支持，为模型训练与测试打造了一个兼具合成特性与高度真实感的环境。 如需咨询、反馈或合作机会，或有意向定制人工数据集，请联系： 姓名：Marko Kojić LinkedIn：https://www.linkedin.com/in/mmkoya 我欢迎所有有意向使用或合作开发人工数据集的研究人员、开发者与机构前来咨询。