7 citations · 17 across the 6 of their papers we have counts for
6 papers · 1 filter
Using simulation to quantify the performance of automotive perception systems
Zhenyi Liu, Devesh Shah, Alireza Rahimpour +3
The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance…
ISETAuto: Detecting vehicles with depth and radiance information
Zhenyi Liu, Joyce Farrell, Brian Wandell
Autonomous driving applications use two types of sensor systems to identify vehicles - depth sensing LiDAR and radiance sensing cameras. We compare the performance (average precisi…
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping
Xiaochen Zhao, Zerong Zheng, Chaonan Ji +5
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utili…
Neural Network Generalization: The impact of camera parameters
Zhenyi Liu, Trisha Lian, Joyce Farrell +1
We quantify the generalization of a convolutional neural network (CNN) trained to identify cars. First, we perform a series of experiments to train the network using one image data…
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network
Zhenyi Liu, Trisha Lian, Joyce Farrell +1
Imaging systems are increasingly used as input to convolutional neural networks (CNN) for object detection; we would like to design cameras that are optimized for this purpose. It…
A system for generating complex physically accurate sensor images for automotive applications
Zhenyi Liu, Minghao Shen, Jiaqi Zhang +4
We describe an open-source simulator that creates sensor irradiance and sensor images of typical automotive scenes in urban settings. The purpose of the system is to support camera…