# REGNet: Ray-Based Enhancement Grouping for 3D Object Detection Based on Point Cloud  
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3 State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS]  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
on projecting point clouds into a bird's-eye-view space [11; 12]. However, bird's-eye-view methods sacrifice depth in the geometric clues that are crucial for clustering indoor scenes, which decrease detection performance. Some other researchers have adopted two-stage cascade approaches to address the issues [13; 14]. In the first stage, 2D object detectors will be used to obtain 2D object bounding boxes. Then, in the second stage, these 2D bounding boxes will be extruded to form the final 3D bounding boxes. Another technical branch is point-based methods. Point clouds have emerged as a great powerful representation for 3D deep learning tasks, such as classification [15; 16; 17; 18; 19; 20], semantic segmentation [21; 22; 23], point cloud normal estimation [24], 3D reconstruction [25; 26; 27], and 3D object detection [28; 29; 30; 31]. Most of these works adopt raw point clouds to extract expressive representations based on pioneering work PointNet/PointNet++ [15; 16]. The first end-to-end point-based work, VoteNet [32], utilizes PointNet++ as the backbone to extract point features, followed by a neural network that reproduces the classic traditional Hough voting scheme. It consists of three main components: a PointNet++-based point feature extraction module, a voting module, and a cluster generation module for object proposal and classification. Based on these three modules, many subsequent works attempt to improve performance by enhancing or modifying each module. Some works propose a combined MLP (CMLP) [33] and attention MLP (AMLP) [28] to enhance the modelling ability of the PointNet++ backbone. Some works consider the voting and clustering generation module not powerful enough, such as MLCVNet [29], which proposes a patch-to-patch context module and object-to-object context modules to capture contextual information to obtain more accurate vote centers and clusters, respectively. RBGNet [34] proposes a ray-based feature grouping module to improve the grouping scheme of VoteNet as well as a foreground-based feature extraction module to enhance feature representation ability. Compared with VoteNet, although the aforementioned methods can achieve many improvements, we find that there is still room for improvement in these three modules. For feature extraction in the backbone network, most methods focus on obtaining more reasonable feature representations. Although RBGNet [34] proposes a foreground-based module to consider different contributions of the foreground points and background points in the input point clouds, it did not consider the different contributions of foreground and background points in the input point cloud, and it did not take into account the relationship between foreground and background points. There is also much work to be done to improve the voting generation part, such as RBGNet [34], which utilizes a ray-based module to learn better representations of object shape. However, the authors did not consider the relationship among the votes on each ray. For the cluster generation and classification part, most of the works ignore the surrounding clusters' contextual information, which is crucial for the current clustering classification. To address the aforementioned issues, this paper discusses the task of indoor scene 3D object detection via point cloud. Specifically, we propose a foreground-based module to calculate the different contributions of the foreground and background points in the backbone. In this paper, we adopt PointNet++ as our backbone network.