# Focusing 3D Small Objects with Object Matching Set Abstraction
###### Abstract  
Currently, 3D object detection methods fail to detect small objects due to the fewer effective points of small objects. It is a significant challenge to reduce the loss of information of points in representation learning. To this end, we propose an effective 3D detection method with object matching set abstraction (OMSA). We observe that key points are lost during feature learning with multiple set abstraction layers, especially for downsampling and queries. Therefore, we present a novel sampling module named focus-based sampling, which raises the sampling probability of small objects. In addition, we design a multi-scale cube query to match the small objects with a close geometric alignment. Our comprehensive experimental evaluations on the KITTI 3D benchmark demonstrate significant performance improvements in 3D object detection. Notably, the proposed framework exhibits competitive detection accuracy for small objects (pedestrians and cyclists). Through an ablation study, we verify that each module contributes to the performance enhancement and demonstrate the robustness of the method against the balance factor. 3D object detection; set abstraction; small object detection; point clouds +
Footnote †: journal: Medical3D-2D conversion or voxelization processes. To circumvent this issue, a second category of methods, point-based methods, has been developed that focuses on immediate feature extraction from unprocessed point cloud data, bypassing intermediate representation conversions. Leveraging learning techniques, such as PointNet [2] and its variants, point-based methods avoid quantization errors. The set abstraction (SA) layers are cascaded to achieve representation learning. The native SA layers are biased toward feature learning of normal objects. The general module of the SA layer contains three components: query, point downsampling, and MLP. Typically, context points are captured by a ball query. Yet, there is a significant difference in the geometric shape between the ball and the object, which may ignore key points and introduce noise points. The key information can be extracted by the operation of downsampling. The number of points associated with small objects is considerably lower compared to that of large objects. When subjected to repeated downsampling processes, critical information on the small objects is often substantially diminished. Consequently, the corresponding detection performance significantly decreases. Aiming at the issues of key point feature loss and geometric shape mismatch during downsampling, we present an efficient semantically guided set abstraction module for 3D object detection. This block explicitly boosts the representation learning for small objects while preserving the detection performance for normal objects. First, focus-based sampling with comprehensive scores is designed with the help of semantic information to raise the sampling probability of key points for small objects. Second, a flexible cube query is carried out to better capture the nearest neighboring points and reduce the introduction of irrelevant points. Our approach exhibits competitive performance when assessed on the KITTI 3D object detection dataset. The ablation study shows that the proposed block can enhance the performance. The contributions can be summarized in threefold:  
1. We design a cube query set abstraction to achieve fine-grained representation learning for small objects. This module employs multi-scale cubes to extract multi-level features and reduces the introduction of irrelevant noise simultaneously. 2. We propose focus-based sampling to reduce feature loss during downsampling. This block utilizes semantic information to enhance the sampling probability of key points in small objects. 3. Extensive experimental evaluations conducted on the KITTI 3D object detection benchmark demonstrate the effectiveness of our method in improving the detection performance for small objects.