Transcription of Pseudo-LiDAR From Visual Depth Estimation: Bridging the ...
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Pseudo-LiDAR From Visual Depth Estimation: Bridging the ...
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous DrivingYan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q. WeinbergerCornell University, Ithaca, NY{yw763, wc635, dg595, bh497, mc288, object detection is an essential task in autonomousdriving. Recent techniques excel with highly accurate de-tection rates, provided the 3D input data is obtained fromprecise but expensive lidar technology. Approaches basedon cheaper monocular or stereo imagery data have, untilnow, resulted in drastically lower accuracies a gap thatis commonly attributed to poor image-based Depth estima-tion. However, in this paper we argue that it is not the qual-ity of the data but its representation that accounts for themajority of the difference. Taking the inner workings of con-volutional neural networks into consideration, we proposeto convert image-based Depth maps to Pseudo-LiDAR repre-sentations essentially mimicking the lidar signal.}
LiDAR is not a discrepancy in depth accuracy, but a poor choice of representations of the 3D information for ConvNet-based 3D object detection systems operating on stereo. Specifically, the LiDAR signal is commonly rep-resented as 3D point clouds [23] or viewed from the top-down “bird’s-eye view” perspective [33], and processed ac-cordingly.
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