Results 101 to 110 of about 21,157 (187)

Memory‐Reduced Convolutional Neural Network for Fast Phase Hologram Generation

Advanced Intelligent Systems, Volume 8, Issue 3, March 2026.
This article reports a lightweight convolutional neural network framework using INT8 quantization to efficiently generate 3D computer‐generated holograms from a single 2D image. The quantized model reduces memory usage and computational cost, accelerates inference speed, and maintains high output quality, enabling real‐time holographic display on low ...
Chenliang Chang, Chenzhou Zhao, Bo Dai, Qi Wang, Dawei Zhang, Songlin Zhuang, Chao Ping Chen   +6 more
wiley   +1 more source

ST-3DView: Multi-Scale Contrast-Enhanced 3D Point Cloud Reconstruction of Single-View Objects From Video Scene Transition

IEEE Access
3D object tracking in monocular video relies on understanding the scene content to improve the continuity of the tracking signal. Reconstructing 3D shapes of single-view objects is essential for capturing object depth, orientation, and position within ...
Dipanita Chakraborty, Werapon Chiracharit, Kosin Chamnongthai   +2 more
doaj   +1 more source

Learning Occupancy for Monocular 3D Object Detection

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to facilitate the learning, but often fail to fully exploit the benefits of three-dimensional feature extraction in frustum and 3D space.
Peng, Liang, Xu, Junkai, Cheng, Haoran, Yang, Zheng, Wu, Xiaopei, Qian, Wei, Wang, Wenxiao, Wu, Boxi, Cai, Deng   +8 more
openaire   +2 more sources

AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features

Applied Sciences
Monocular 3D object detection is a challenging task in autonomous systems due to the lack of explicit depth information in single-view images. Existing methods often depend on external depth estimators or expensive sensors, which increase computational ...
Ruochen Zhang, Hyeung-Sik Choi, Dongwook Jung, Phan Huy Nam Anh, Sang-Ki Jeong, Zihao Zhu   +5 more
doaj   +1 more source

BEVCorner: Enhancing Bird’s-Eye View Object Detection with Monocular Features via Depth Fusion

Applied Sciences
This research paper presents BEVCorner, a novel framework that synergistically integrates monocular and multi-view pipelines for enhanced 3D object detection in autonomous driving. By fusing depth maps from Bird’s-Eye View (BEV) with object-centric depth
Jesslyn Nathania, Qiyuan Liu, Zhiheng Li, Liming Liu, Yipeng Gao   +4 more
doaj   +1 more source

MSFNet3D: Monocular 3D Object Detection via Dual-Branch Depth-Consistent Fusion and Semantic-Guided Point Cloud Refinement

World Electric Vehicle Journal
The rapid development of autonomous driving has underscored the pivotal role of 3D perception. Monocular 3D object detection, as a cost-effective alternative to expensive lidar systems, is garnering increasing attention.
Rong Yang, Zhijie You, Renhui Luo
doaj   +1 more source

PLC-Fusion: Perspective-Based Hierarchical and Deep LiDAR Camera Fusion for 3D Object Detection in Autonomous Vehicles

Information
Accurate 3D object detection is essential for autonomous driving, yet traditional LiDAR models often struggle with sparse point clouds. We propose perspective-aware hierarchical vision transformer-based LiDAR-camera fusion (PLC-Fusion) for 3D object ...
Husnain Mushtaq, Xiaoheng Deng, Fizza Azhar, Mubashir Ali, Hafiz Husnain Raza Sherazi   +4 more
doaj   +1 more source

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

Connection Science
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, these methods have not achieved satisfactory results. One of
Gang Liu, Xiaoxiao Xie, Qingchen Yu
doaj   +1 more source

A survey on 3D object detection in real time for autonomous driving

Frontiers in Robotics and AI
This survey reviews advances in 3D object detection approaches for autonomous driving. A brief introduction to 2D object detection is first discussed and drawbacks of the existing methodologies are identified for highly dynamic environments. Subsequently,
Marcelo Contreras, Aayush Jain, Neel P. Bhatt, Arunava Banerjee, Ehsan Hashemi   +4 more
doaj   +1 more source

MonoAMP: Adaptive Multi-Order Perceptual Aggregation for Monocular 3D Vehicle Detection

Sensors
Monocular 3D object detection is rapidly emerging as a key research direction in autonomous driving, owing to its resource efficiency and ease of implementation.
Xiaoxi Hu, Tao Chen, Wentao Zhang, Guangyi Ji, Hongxia Jia   +4 more
doaj   +1 more source