论文标题
UKPGAN：一般自我监视的关键点探测器
UKPGAN: A General Self-Supervised Keypoint Detector
论文作者
论文摘要
关键点检测是对象注册和对齐的重要组成部分。在这项工作中，我们认为关键点检测是信息压缩，并迫使模型提取物体无关的点。基于此，我们提出了UKPGAN，这是一种一般自我监督的3D键盘检测器，在该检测器中检测到关键点，以便它们可以重建原始对象形状。两个模块：提出了基于GAN的Kepoint稀疏控制和显着信息蒸馏模块来定位这些重要的关键点。广泛的实验表明，我们的关键点与人体注释的按键标签很好地对齐，并且可以应用于各种非刚性变形的SMPL人体。此外，我们对清洁物体集合训练的训练器探测器可以很好地概括为现实世界的场景，因此，与现成的点描述符结合使用时，进一步改善了几何注册。可重复性实验表明，我们的模型在刚性和非刚性转换下都是稳定的，并进行了局部参考框架估计。我们的代码可在https://github.com/qq456cvb/ukpgan上找到。
Keypoint detection is an essential component for the object registration and alignment. In this work, we reckon keypoint detection as information compression, and force the model to distill out irrelevant points of an object. Based on this, we propose UKPGAN, a general self-supervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape. Two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints. Extensive experiments show that our keypoints align well with human annotated keypoint labels, and can be applied to SMPL human bodies under various non-rigid deformations. Furthermore, our keypoint detector trained on clean object collections generalizes well to real-world scenarios, thus further improves geometric registration when combined with off-the-shelf point descriptors. Repeatability experiments show that our model is stable under both rigid and non-rigid transformations, with local reference frame estimation. Our code is available on https://github.com/qq456cvb/UKPGAN.
