论文标题
蛇：形状感知神经3D关键点字段
SNAKE: Shape-aware Neural 3D Keypoint Field
论文作者
论文摘要
从点云中检测3D关键点对于形状重建很重要，而这项工作研究了双重问题：形状重建益处3D Keypoint检测？现有方法要么根据不同订单的统计数据寻求显着特征，要么学会预测转换不变的关键点。然而，将形状重建纳入3D关键点检测的想法尚未探索。我们认为这受到以前的问题表述的限制。为此，提出了一个名为Snake的新型无监督范式，这是形状感知神经3D关键点字段的缩写。与最近的基于坐标的辐射或距离场相似，我们的网络将3D坐标作为输入，并同时预测隐式形状指标和关键点显着性，因此自然纠缠了3D键盘检测和形状重建。我们在各种公共基准测试中取得了卓越的性能，包括独立对象数据集ModelNet40，KeyPointNet，SMPL网格和场景级数据集3DMatch和Redwood。固有的形状意识带来了以下几个优点。 （1）蛇会产生与人类语义注释一致的3D关键，即使没有这种监督。 （2）在重复性方面，蛇的表现优于对应，尤其是当输入点云下采样时。 （3）生成的关键点允许准确的几何登记，尤其是在零拍设置中。代码可从https://github.com/zhongcl-thu/snake获得
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection? Existing methods either seek salient features according to statistics of different orders or learn to predict keypoints that are invariant to transformation. Nevertheless, the idea of incorporating shape reconstruction into 3D keypoint detection is under-explored. We argue that this is restricted by former problem formulations. To this end, a novel unsupervised paradigm named SNAKE is proposed, which is short for shape-aware neural 3D keypoint field. Similar to recent coordinate-based radiance or distance field, our network takes 3D coordinates as inputs and predicts implicit shape indicators and keypoint saliency simultaneously, thus naturally entangling 3D keypoint detection and shape reconstruction. We achieve superior performance on various public benchmarks, including standalone object datasets ModelNet40, KeypointNet, SMPL meshes and scene-level datasets 3DMatch and Redwood. Intrinsic shape awareness brings several advantages as follows. (1) SNAKE generates 3D keypoints consistent with human semantic annotation, even without such supervision. (2) SNAKE outperforms counterparts in terms of repeatability, especially when the input point clouds are down-sampled. (3) the generated keypoints allow accurate geometric registration, notably in a zero-shot setting. Codes are available at https://github.com/zhongcl-thu/SNAKE
