Evaluation and Optimization of Rendering Techniques for Autonomous Driving Simulation

06/27/2023
by   Chengyi Wang, et al.
0

In order to meet the demand for higher scene rendering quality from some autonomous driving teams (such as those focused on CV), we have decided to use an offline simulation industrial rendering framework instead of real-time rendering in our autonomous driving simulator. Our plan is to generate lower-quality scenes using a game engine, extract them, and then use an IQA algorithm to validate the improvement in scene quality achieved through offline rendering. The improved scenes will then be used for training.

READ FULL TEXT

page 3

page 4

research
02/01/2023

Development of Real-time Rendering Technology for High-Precision Models in Autonomous Driving

Our autonomous driving simulation lab produces a high-precision 3D model...
research
05/11/2022

READ: Large-Scale Neural Scene Rendering for Autonomous Driving

Synthesizing free-view photo-realistic images is an important task in mu...
research
06/30/2013

Progressive Blue Surfels

In this paper we describe a new technique to generate and use surfels fo...
research
02/29/2020

An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice

The autonomous driving (AD) industry is exploring the use of knowledge g...
research
09/13/2018

On Offline Evaluation of Vision-based Driving Models

Autonomous driving models should ideally be evaluated by deploying them ...
research
08/02/2022

Procedural Generation and Rendering of Realistic, Navigable Forest Environments: An Open-Source Tool

Simulation of forest environments has applications from entertainment an...
research
06/26/2019

ORRB – OpenAI Remote Rendering Backend

We present the OpenAI Remote Rendering Backend (ORRB), a system that all...

Please sign up or login with your details

Forgot password? Click here to reset