activity
20182023
most citedEnd-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

9 citations · 15 across the 3 of their papers we have counts for

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Showing cs.CVShow all

5 papers · 1 filter

cs.CV2023★ 1 cited

Learning to Detect Touches on Cluttered Tables

Norberto Adrian Goussies, Kenji Hata, Shruthi Prabhakara +28

We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables. We propose a real-time, on-device, learni…

cs.CV2020

Wasserstein Distances for Stereo Disparity Estimation

Divyansh Garg, Yan Wang, Bharath Hariharan +3

Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or dispar…

cs.CV2020★ 9 cited

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

Rui Qian, Divyansh Garg, Yan Wang +6

Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they…

cs.CV2019

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

Yurong You, Yan Wang, Wei-Lun Chao +5

Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate dep…

cs.CV2018

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Yan Wang, Wei-Lun Chao, Divyansh Garg +3

3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise bu…