Abstract
Point clouds acquired with LiDAR are widely adopted in various fields, such as three-dimensional (3D) reconstruction, autonomous driving, and robotics. However, the high-density point cloud of large scenes captured with Lidar usually contains a large number of virtual points generated by the specular reflections of reflective materials, such as glass. When applying such large-scale high-density point clouds, reflection noise may have a significant impact on 3D reconstruction and other related techniques. In this study, we propose a method that uses deep learning and multi-position sensor comparison method to remove noise due to reflections from high-density point clouds in large scenes. The proposed method converts large-scale high-density point clouds into a range image and subsequently uses a deep learning method and multi-position sensor comparison method for noise detection. This alleviates the limitation of the deep learning networks, specifically their inability to handle large-scale high-density point clouds. The experimental results show that the proposed algorithm can effectively detect and remove noise due to reflection.
Highlights
With rapid advances in three-dimensional (3D) acquisition technology, various types of 3D sensors, such as 3D scanners and light detection and ranging (LiDAR), are becoming increasingly popular
In other words, when using a LiDAR scanner to capture a 3D real scene, a single laser pulse from the scanner initially hits the surface of reflective materials, and its echo pulse returns to the scanner to create a 3D point on the glass plane
This study proposes the use of a reflection value range image extracted from 3D LiDAR data as the input, which is combined with a transformer auto-encoder network to detect the noise generated by an object with high reflection intensity in the point-cloud data
Summary
With rapid advances in three-dimensional (3D) acquisition technology, various types of 3D sensors, such as 3D scanners and light detection and ranging (LiDAR), are becoming increasingly popular. LiDAR sensors measure the distance between the scanner and target object by transmitting laser pulses and receiving the return pulses. Reflective materials (such as glass, displays, and walls with smooth surfaces) cause the return pulses received by the LiDAR to be perceived as linear reflected pulses reaching the scanned object. In other words, when using a LiDAR scanner to capture a 3D real scene, a single laser pulse from the scanner initially hits the surface of reflective materials, and its echo pulse returns to the scanner to create a 3D point on the glass plane.
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