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20172021
most citedCLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

41 citations · 55 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV2021

Full-Velocity Radar Returns by Radar-Camera Fusion

Yunfei Long, Daniel Morris, Xiaoming Liu +3

A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object ve…

cs.CV20211 cited

Radar-Camera Pixel Depth Association for Depth Completion

Yunfei Long, Daniel Morris, Xiaoming Liu +3

While radar and video data can be readily fused at the detection level, fusing them at the pixel level is potentially more beneficial. This is also more challenging in part due to…

cs.CV2021

Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries

Saif Imran, Xiaoming Liu, Daniel Morris

Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and…

cs.CV202041 cited

CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

Su Pang, Daniel Morris, Hayder Radha

There have been significant advances in neural networks for both 3D object detection using LiDAR and 2D object detection using video. However, it has been surprisingly difficult to…

cs.CV2019

Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis

Yunfei Long, Amber Bassett, Karen Cichy +2

Splits on canned beans appear in the process of preparation and canning. Researchers are studying how they are influenced by cooking environment and genotype. However, there is no…

cs.CV2019

Depth Coefficients for Depth Completion

Saif Imran, Yunfei Long, Xiaoming Liu +1

Depth completion involves estimating a dense depth image from sparse depth measurements, often guided by a color image. While linear upsampling is straight forward, it results in a…