We present an approach that uses a multi-camera system to train fine-grained detectors for keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure multiview bootstrapping: first, an initial keypoint detector is used to produce noisy labels in multiple views of the hand. The noisy detections are then triangulated in 3D using multiview geometry or marked as outliers. Finally, the reprojected triangulations are used as new labeled training data to improve the detector. We repeat this process, generating more labeled data in each iteration. We derive a result analytically relating the minimum number of views to achieve target true and false positive rates for a given detector. The method is used to train a hand keypoint detector for single images. The resulting keypoint detector runs in realtime on RGB images and has accuracy comparable to methods that use depth sensors. The single view detector, triangulated over multiple views, enables 3D markerless hand motion capture with complex object interactions.

该研究提出了一种利用多相机系统训练微粒子探测器的方法，采用多视角几何或标记为异常值对噪声标签进行三角测量来改善探测器，从而实现单幅图像的手指关键点检测，并使该技术成为实现具有复杂对象交互的无标记3D手部动作捕捉的解决方案。

利用多视角引导的单张图像手部关键点检测