scholarly journals Efficient 3D Object Detection of Indoor Scenes Based on RGB-D Video Stream

2021 ◽  
Vol 33 (7) ◽  
pp. 1015-1025
Author(s):  
Yongwei Miao ◽  
Jiahui Chen ◽  
Xinjie Zhang ◽  
Wenjuan Ma ◽  
Shusen Sun
Keyword(s):  
Object Detection ◽  
Video Stream ◽  
3D Object ◽  
Indoor Scenes ◽  
3D Object Detection

scholarly journals 3D Object Detection Using Scale Invariant and Feature Reweighting Networks

2019 ◽  
Vol 33 ◽  
pp. 9267-9274 ◽  
Author(s):  
Xin Zhao ◽  
Zhe Liu ◽  
Ruolan Hu ◽  
Kaiqi Huang
Keyword(s):  
Object Detection ◽  
Network Architecture ◽  
Point Clouds ◽  
Scale Invariant ◽  
3D Object ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
Bounding Boxes ◽  
The One ◽  
3D Object Detection

3D object detection plays an important role in a large number of real-world applications. It requires us to estimate the localizations and the orientations of 3D objects in real scenes. In this paper, we present a new network architecture which focuses on utilizing the front view images and frustum point clouds to generate 3D detection results. On the one hand, a PointSIFT module is utilized to improve the performance of 3D segmentation. It can capture the information from different orientations in space and the robustness to different scale shapes. On the other hand, our network obtains the useful features and suppresses the features with less information by a SENet module. This module reweights channel features and estimates the 3D bounding boxes more effectively. Our method is evaluated on both KITTI dataset for outdoor scenes and SUN-RGBD dataset for indoor scenes. The experimental results illustrate that our method achieves better performance than the state-of-the-art methods especially when point clouds are highly sparse.

2D-to-3D Projection for Monocular and Multi-View 3D Multi-class Object Detection in Indoor Scenes

2021 ◽  
Vol 12 (9) ◽  
pp. 459-469
Author(s):  
D. D. Rukhovich ◽  
Keyword(s):  
Path Planning ◽  
Object Detection ◽  
Mobile Robots ◽  
3D Object ◽  
Indoor Scenes ◽  
Novel Method ◽  
Semantic Scene ◽  
Almost All ◽  
3D Object Detection ◽  
2D To 3D

In this paper, we propose a novel method of joint 3D object detection and room layout estimation. The proposed method surpasses all existing methods of 3D object detection from monocular images on the indoor SUN RGB-D dataset. Moreover, the proposed method shows competitive results on the ScanNet dataset in multi-view mode. Both these datasets are collected in various residential, administrative, educational and industrial spaces, and altogether they cover almost all possible use cases. Moreover, we are the first to formulate and solve a problem of multi-class 3D object detection from multi-view inputs in indoor scenes. The proposed method can be integrated into the controlling systems of mobile robots. The results of this study can be used to address a navigation task, as well as path planning, capturing and manipulating scene objects, and semantic scene mapping.

Dense-JANet for Monocular 3D Object Detection

2020 ◽  
Author(s):  
Xiaoqing Shang ◽  
Zhiwei Cheng ◽  
Su Shi ◽  
Zhuanghao Cheng ◽  
Hongcheng Huang
Keyword(s):  
Object Detection ◽  
3D Object ◽  
3D Object Detection

Dense Point Diffusion for 3D Object Detection

2020 ◽  
Author(s):  
Xu Liu ◽  
Jiayan Cao ◽  
Qianqian Bi ◽  
Jian Wang ◽  
Boxin Shi ◽  
...  
Keyword(s):  
Object Detection ◽  
3D Object ◽  
Dense Point ◽  
3D Object Detection

scholarly journals RoIFusion: 3D Object Detection from LiDAR and Vision

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Can Chen ◽  
Luca Zanotti Fragonara ◽  
Antonios Tsourdos
Keyword(s):  
Object Detection ◽  
3D Object ◽  
3D Object Detection

scholarly journals A Two-Stage Data Association Approach for 3D Multi-Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2894
Author(s):  
Minh-Quan Dao ◽  
Vincent Frémont
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Moving Objects ◽  
Data Association ◽  
Autonomous Driving ◽  
Tracking Accuracy ◽  
Two Stage ◽  
Bipartite Matching ◽  
3D Object ◽  
3D Object Detection

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set.

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