Monocular 3D object detection with thermodynamic loss and decoupled instance depth

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-02-13 DOI:10.1080/09540091.2024.2316022

Gang Liu, Xiaoxiao Xie, Qingchen Yu

引用次数: 0

Abstract

Monocular 3D detection is to obtain the 3D information of the object from the image. The mainstream methods mainly use L1 loss or L1-like loss to control the instance depth prediction. However, the...

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具有热力学损失和解耦实例深度的单目三维物体检测

单目三维检测是从图像中获取物体的三维信息。主流方法主要使用 L1 损失或 L1-like 损失来控制实例深度预测。然而，L1损失...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.

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