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KNN improved Transformer for 3D object detection
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DOI：10.1016/j.image.2026.117488.png)
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Abstract
En 中文
In recent years, 3D object detection in autonomous driving perception has gained significant attention in the industry. Due to its characteristics, LiDAR has become the most commonly used and essential sensor. However, voxel-based networks often lose context information during the voxelization process, which negatively impacts the detection of small objects. In this paper, we address the challenge of low accuracy in LiDAR-based detection, especially for small object categories, by proposing an improved Transformer structure. Transformers are a type of deep learning model known for their ability to capture long-range dependencies and contextual relationships in data. In our approach, we incorporate a k-Nearest Neighbors (KNN) algorithm, which is a method for identifying the closest points in space, to enhance the spatial relationships between point clouds. This combination allows the model to better capture context information, strengthen feature extraction, and significantly reduce both missed and false detections. Our method is designed to be plug-and-play, allowing it to be directly applied to existing point cloud detectors. We evaluate our approach on the public KITTI and Astyx datasets. Experimental results show significant improvements, especially in detecting small object categories, even in challenging conditions.
Keywords:
Autonomous vehicle
Transformer
Lidar
3D object detection
Point cloud
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2.7
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18
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