从一张真实池塘照片中自动推荐最佳击球方案，让AI当你的台球教练。
pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild
- 用单张图像检测球桌和球的位置，精度达0.4厘米
- 构建标准化强化学习环境，实现94.7%单次击球成功率
- 首次实现单轮清台成功率达30%，适合智能体育辅助系统研究者
计算机视觉模型在体育领域应用日益广泛，强化学习（RL）也已在国际象棋、围棋等策略游戏中超越人类。本文聚焦经典8球台球，提出pix2pockets——一个基于强化学习的台球教练基础框架。给定一张真实场景下的球桌图像，先检测球桌与所有球体，再推荐最优击球方案。为此，我们构建了包含195张多样图像的数据集，人工标注了全部球体与球桌标记点，生成5748个物体分割掩码。针对击球建议任务，我们设计了一个标准化的强化学习环境，便于算法开发与评估。目标检测模型在AP50上达到91.2，球位定位误差仅0.4厘米。我们对比多种标准强化学习算法，发现均无法在不犯规的情况下完成全部进球。同时提出一个简单基线方法，单次击球成功率达94.7%，并在30%情况下实现单轮清台。
原文摘要 · Abstract (English)
Computer vision models have seen increased usage in sports, and reinforcement learning (RL) is famous for beating humans in strategic games such as Chess and Go. In this paper, we are interested in building upon these advances and examining the game of classic 8-ball pool. We introduce pix2pockets, a foundation for an RL-assisted pool coach. Given a single image of a pool table, we first aim to detect the table and the balls and then propose the optimal shot suggestion. For the first task, we build a dataset with 195 diverse images where we manually annotate all balls and table dots, leading to 5748 object segmentation masks. For the second task, we build a standardized RL environment that allows easy development and benchmarking of any RL algorithm. Our object detection model yields an AP50 of 91.2 while our ball location pipeline obtains an error of only 0.4 cm. Furthermore, we compare standard RL algorithms to set a baseline for the shot suggestion task and we show that all of them fail to pocket all balls without making a foul move. We also present a simple baseline that achieves a per-shot success rate of 94.7% and clears a full game in a single turn 30% of the time.
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