Recognition and pose estimation of primitive shapes from depth images for spatial augmented reality

2016 ◽  
Author(s):  
Ryo Hachiuma ◽  
Hideo Saito
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Depth Images ◽  
Spatial Augmented Reality

scholarly journals RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1299
Author(s):  
Honglin Yuan ◽  
Tim Hoogenkamp ◽  
Remco C. Veltkamp
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
3D Models ◽  
Depth Image ◽  
Great Success ◽  
Estimation Algorithms ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Image Pairs ◽  
Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

Spatial augmented reality

2020 ◽  
Author(s):  
Tim Bosch ◽  
Gu van Rhijn ◽  
Frank Krause ◽  
Reinier Könemann ◽  
Ellen S. Wilschut ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Spatial Augmented Reality

scholarly journals Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

Sensors ◽  
2015 ◽  
Vol 15 (6) ◽  
pp. 12410-12427 ◽  
Author(s):  
Hanguen Kim ◽  
Sangwon Lee ◽  
Dongsung Lee ◽  
Soonmin Choi ◽  
Jinsun Ju ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Human Pose Estimation ◽  
Svm Classifier ◽  
Depth Images ◽  
Human Pose

Interactive Dance Support System Using Spatial Augmented Reality

2021 ◽  
Author(s):  
Yi He ◽  
Xiaojie Zheng ◽  
Asuka Yagami ◽  
Yichen Peng ◽  
Shogo Yoshida ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Support System ◽  
Spatial Augmented Reality

scholarly journals Face-from-Depth for Head Pose Estimation on Depth Images

2020 ◽  
Vol 42 (3) ◽  
pp. 596-609 ◽  
Author(s):  
Guido Borghi ◽  
Matteo Fabbri ◽  
Roberto Vezzani ◽  
Simone Calderara ◽  
Rita Cucchiara
Keyword(s):  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Depth Images

Spatial augmented reality as a method for a mobile robot to communicate intended movement

2014 ◽  
Vol 34 ◽  
pp. 241-248 ◽  
Author(s):  
Michael D. Coovert ◽  
Tiffany Lee ◽  
Ivan Shindev ◽  
Yu Sun
Keyword(s):  
Augmented Reality ◽  
Mobile Robot ◽  
Spatial Augmented Reality

scholarly journals CSA6D: Channel-Spatial Attention Networks for 6D Object Pose Estimation

2021 ◽  
Author(s):  
Tao Chen ◽  
Dongbing Gu
Keyword(s):  
Spatial Attention ◽  
Pose Estimation ◽  
Estimation Accuracy ◽  
Pose Prediction ◽  
Attention Networks ◽  
Depth Images ◽  
Segmented Object ◽  
Benchmark Datasets ◽  
Object Pose Estimation ◽  
Network Channel

Abstract6D object pose estimation plays a crucial role in robotic manipulation and grasping tasks. The aim to estimate the 6D object pose from RGB or RGB-D images is to detect objects and estimate their orientations and translations relative to the given canonical models. RGB-D cameras provide two sensory modalities: RGB and depth images, which could benefit the estimation accuracy. But the exploitation of two different modality sources remains a challenging issue. In this paper, inspired by recent works on attention networks that could focus on important regions and ignore unnecessary information, we propose a novel network: Channel-Spatial Attention Network (CSA6D) to estimate the 6D object pose from RGB-D camera. The proposed CSA6D includes a pre-trained 2D network to segment the interested objects from RGB image. Then it uses two separate networks to extract appearance and geometrical features from RGB and depth images for each segmented object. Two feature vectors for each pixel are stacked together as a fusion vector which is refined by an attention module to generate a aggregated feature vector. The attention module includes a channel attention block and a spatial attention block which can effectively leverage the concatenated embeddings into accurate 6D pose prediction on known objects. We evaluate proposed network on two benchmark datasets YCB-Video dataset and LineMod dataset and the results show it can outperform previous state-of-the-art methods under ADD and ADD-S metrics. Also, the attention map demonstrates our proposed network searches for the unique geometry information as the most likely features for pose estimation. From experiments, we conclude that the proposed network can accurately estimate the object pose by effectively leveraging multi-modality features.

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