Enhanced 3D perception using Laser based scanning display

2021 ◽  
Author(s):  
Ran Gabai ◽  
Gil Cahana ◽  
Meni Yehiel ◽  
Gady Yearim ◽  
Telman Yusupov ◽  
...  
Keyword(s):  
3D Perception

scholarly journals Towards an Efficient Tomato Harvesting Robot: 3D Perception, Manipulation, and End-Effector

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 17631-17640
Author(s):  
Jongpyo Jun ◽  
Jeongin Kim ◽  
Jaehwi Seol ◽  
Jeongeun Kim ◽  
Hyoung Il Son
Keyword(s):  
3D Perception ◽  
End Effector ◽  
Harvesting Robot

Simultaneous 2D and 3D perception for stereoscopic displays based on polarized or active shutter glasses

2014 ◽  
Vol 25 (4) ◽  
pp. 622-631 ◽  
Author(s):  
Payman Aflaki ◽  
Miska M. Hannuksela ◽  
Hamed Sarbolandi ◽  
Moncef Gabbouj
Keyword(s):  
3D Perception ◽  
Stereoscopic Displays ◽  
2D And 3D

scholarly journals Task-Aware Monocular Depth Estimation for 3D Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12257-12264 ◽  
Author(s):  
Xinlong Wang ◽  
Wei Yin ◽  
Tao Kong ◽  
Yuning Jiang ◽  
Lei Li ◽  
...  
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Depth Estimation ◽  
3D Perception ◽  
Research Attention ◽  
3D Object ◽  
Depth Prediction ◽  
Monocular Depth ◽  
Almost All ◽  
3D Object Detection

Monocular depth estimation enables 3D perception from a single 2D image, thus attracting much research attention for years. Almost all methods treat foreground and background regions (“things and stuff”) in an image equally. However, not all pixels are equal. Depth of foreground objects plays a crucial role in 3D object recognition and localization. To date how to boost the depth prediction accuracy of foreground objects is rarely discussed. In this paper, we first analyze the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground and background depth using separate optimization objectives and decoders. Our method significantly improves the depth estimation performance on foreground objects. Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new state-of-the-art results among other monocular methods. Code will be available at: https://github.com/WXinlong/ForeSeE.

scholarly journals Fast 3D Perception for Collision Avoidance and SLAM in Domestic Environments

10.5772/8995 ◽  
2010 ◽  
Author(s):  
Dirk Holz ◽  
David Droeschel ◽  
Sven Behnke ◽  
Stefan May ◽  
Hartmut Surm
Keyword(s):  
Collision Avoidance ◽  
3D Perception ◽  
Domestic Environments

scholarly journals Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine

2019 ◽  
Vol 9 (13) ◽  
pp. 2732 ◽  
Author(s):  
Radosław Gierwiało ◽  
Marcin Witkowski ◽  
Maciej Kosieradzki ◽  
Wojciech Lisik ◽  
Łukasz Groszkowski ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Spatial Perception ◽  
Spatial Relationship ◽  
Guidance System ◽  
Training And Education ◽  
3D Perception ◽  
Augmented Reality System ◽  
Internal Structures ◽  
Medical Augmented Reality ◽  
Liver Phantom

This paper presents a projection-based augmented-reality system (MARVIS) that supports the visualization of internal structures on the surface of a liver phantom. MARVIS is endowed with three key features: tracking of spatial relationship between the phantom and the operator’s head in real time, monoscopic projection of internal liver structures onto the phantom surface for 3D perception without additional head-mounted devices, and phantom internal electronic circuit to assess the accuracy of a syringe guidance system. An initial validation was carried out by 25 medical students (12 males and 13 females; mean age, 23.12 years; SD, 1.27 years) and 3 male surgeons (mean age, 43.66 years; SD, 7.57 years). The validation results show that the ratio of failed syringe insertions was reduced from 50% to 30% by adopting the MARVIS projection. The proposed system suitably enhances a surgeon’s spatial perception of a phantom internal structure.

Sign in / Sign up

Export Citation Format

Share Document