# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
## 2 Related Work  
Keypoint Detection: Keypoint detection on point clouds is a crucial step in many computer vision tasks, as keypoints provide a compact yet informative representation of 3D shapes. Early works in this field often focused on extensions of traditional 2D keypoint detectors for 3D data. The 3D scale-invariant feature transform (3D-SIFT) [11] and the 3D Harris detector [12] are two notable examples of such extensions. In the era of deep learning, PointNet [6] was one of the pioneering architectures for point cloud processing, which directly consumes point clouds and learns high-level features. Several keypoint detection methods have built upon PointNet. For instance, PointNetLK [13] introduced a point cloud registration method based on PointNet, which could also be used for keypoint detection. KPConv [14] is another significant approach that introduced a convolutional-like operation for point clouds. It employs a set of deformable convolutional kernels to adapt to the local geometry of point clouds, which has been shown to be effective for keypoint detection tasks. More recently, methods such as PointCNN [15] and DGCNN [9] have been introduced, which employ different architectures and strategies for learning features from point clouds. These architectures have also been used for keypoint detection tasks, thereby showing promising results. PointCNN [15] introduces a novel convolution operation, X-Conv, for unordered point clouds. It learns a transformation matrix to reorder the input points into a latent and potentially canonical order, which makes the convolution operation possible. The network can be trained to predict a heatmap for each keypoint, where the maxima of the heatmaps correspond to the detected keypoints. DGCNN [9] introduces a new layer called EdgeConv, which operates on a graph that is dynamically constructed from the input point cloud. For each point, the k-nearest neighbors are found, and features are aggregated from these neighbors. The EdgeConv layers help capture local geometric patterns and relationships between points, which are important for identifying keypoints. Deep Learning on Point Clouds: Deep learning on point clouds has become a burgeoning field due to the growing availability of 3D data from sensors such as LiDAR and the increasing demand for 3D understanding in applications like autonomous vehicles, robotics, and augmented reality. One of the seminal works in this field is PointNet [6], which proposed a deep learning architecture specifically designed for point cloud data. The work of [6] uses a series of fully connected layers to process points individually, followed by a global max pooling layer to capture the global features of the point cloud. Generally speaking, it can be divided into the following aspects: point-based [14; 15; 16; 17], graph-based [18; 19; 20; 21; 22], sparse voxel-based [23; 24; 25; 26], spatial CNN-based [27; 28; 29; 22] and, more recently, transformer-based [30; 31; 32; 33; 34]. Attention Mechanism/Transformer-based Segmentation: Transformer-based models have recently been applied to the task of point cloud part segmentation with promising results [35; 32; 36; 37; 38; 39; 40]. One such work is the Point Transformer [41], which introduces a point attention layer that operates on the point cloud data. This layer enhances the features by capturing global context through a self-attention mechanism, which is beneficial for part segmentation tasks. The work of [38] groups potential features from point clouds into superpoints and directly predicts instances through query vectors. Their model can capture the instance information through the superpoint crossattention mechanism. The work of [42] combines the transformer architecture with a U-Net-like structure for semantic segmentation tasks. Although originally proposed for 2D images, SegFormer can be adapted for 3D point cloud part segmentation. The transformer layers in SegFormer enable the model to capture long-range dependencies between points in the cloud, which is crucial for accurate segmentation.