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Stereoscopic Vision Recalling Memory for Monocular 3D Object Detection
IEEE Transactions on Image Processing, 2023Monocular 3D object detection has drawn increasing attention in various human-related applications, such as autonomous vehicles, due to its cost-effective property. On the other hand, a monocular image alone inherently contains insufficient information to infer the 3D information.
Jung Uk Kim, Hyung-Il Kim, Yong Man Ro
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MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks
IEEE Transactions on Image Processing, 2020Monocular 3D object detection has the merit of low cost and can be served as an auxiliary module for autonomous driving system, becoming a growing concern in recent years. In this paper, we present a monocular 3D object detection method with feature enhancement networks, which we call MonoFENet. Specifically, with the estimated disparity from the input
Wentao Bao, Bin Xu, Zhenzhong Chen
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MonoEF: Extrinsic Parameter Free Monocular 3D Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the lack of insight in industrial application, existing methods on open datasets neglect the camera pose ...
Yunsong Zhou +5 more
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Dense-JANet for Monocular 3D Object Detection
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 20203D object detection is one of the vital tasks in many fields, especially for autonomous driving. Among all technology paths for this task, the detection based on monocular images has been proven an efficient way with low cost. However, the performance of most current algorithms is far from satisfactory.
Xiaoqing Shang +4 more
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3D Object Detection from Consecutive Monocular Images
2021Detecting objects in 3D space plays an important role in scene understanding for real applications, such as autonomous driving and mobile robot navigation. Many image-based methods have been proposed due to the high cost of LiDAR. However, monocular images are lack of depth information and difficult to detect objects with occlusion.
Chia-Chun Cheng, Shang-Hong Lai
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Monocular 3D Object Detection for Autonomous Driving
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016The goal of this paper is to perform 3D object detection from a single monocular image in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain highquality object detections.
Xiaozhi Chen +5 more
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Monocular 3D object detection for construction scene analysis
Computer-Aided Civil and Infrastructure Engineering, 2023AbstractThree‐dimensional (3D) object detection, that is, localizing and classifying all critical objects in a 3D space, is essential for downstream construction scene analysis tasks. However, accurate instance segmentation, few 2D object segmentation and 3D object detection data sets, high‐quality feature representations for depth estimation, and ...
Jie Shen +3 more
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