Research Papers

Research Overview

You can find some of the research material related to Google Tango here.

Videos

The video shown in IO'16 about research progress.

Papers

You can find some papers related to Tango in this section. I will update it with more papers when I get some more free time. Some of these papers are on Tango and some others are some related papers which I find useful. I would also recommend going through the references of these papers.

3D Reconstruction

  1. "CHISEL: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially-Hashed Signed Distance Fields" RSS 11.The paper can be found here. It describes the algorithm used by the constructor app.
  2. "RTAB-Map". The app can be found here. The code, papers and videos can be found here.

Localization

  1. "Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization". There is a video about it here. The paper can be found here.
  2. "Placeless Place-Recognition". There is a video about it here. The paper can be found here

Mobile Photogrammetry

  1. "Accurate, Dense, and Robust Multi-View Stereopsis" PAMI 08. The paper can be found here. There is a video about it here.

I would also recommend reading this paper: "Massively Parallel Multiview Stereopsis by Surface Normal Diffusion" ICCV 2015.

Moving Object Detection and Removal

  1. "Reshaping Our Model of the World Over Time" ICRA 16. The paper can be found here (It requires IEEE Xplore membership). There is a video about it here.

I would also recommend reading this paper: "Dense 3D Semantic Mapping of Indoor Scenes from RGB-D Images" ICRA 14.

Outdoor Reconstruction

  1. "3D Modelling on the Go: Interactive 3D Reconstruction of Large-Scale Scenes on Mobile Devices" 3DV 15. The paper can be found here. The official page with video and additional material can be found here.

Area Learning

  1. "BRISK: Binary Robust Invariant Scalable Keypoints" ICCV 11. The paper can be found here. It is used in area learning descriptors. The source code can be found here.
  2. "FREAK: Fast Retina Keypoint" CVPR 2012. The paper can be found here. It is used in area learning descriptors.

Some other useful papers are ORB and BRIEF.

3D Object Recognition (NN based)

  1. "VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition" IROS 15. The paper can be found here.
  2. "3D ShapeNets: A Deep Representation for Volumetric Shapes" CVPR 15. The paper can be found here.

If you don't have any background with CNN, I would recommend going through this one first. It is an infamous paper by Alex Krizhevsky. It is one of the papers that started the CNN craze:

  1. "ImageNet Classification with Deep Convolutional Neural Networks" NIPS 12. The paper can be found here.

Location Recognition

I am mentioning this because it was funded by Google's Tango team:

  1. "Large-Scale Location Recognition and the Geometric Burstiness Problem" CVPR 16. The paper can be found here.

Visual Assistance

  1. "A GPU-Accelerated Real-Time Contextual Awareness Application for the Visually Impaired on Google’s Project Tango Device", Rabia Jafri, The Journal of Supercomputing, Springer, 2016, Volume 73, Issue 2, pp. 887-899 http://dx.doi.org/10.1016/j.ijcci.2016.12.001
  2. "Utilizing the Google Project Tango Tablet Development Kit and the Unity Engine for Image and Infrared Data-Based Obstacle Detection for the Visually Impaired", Rabia Jafri, Rodrigo Louzada Campos, Syed Abid Ali and Hamid R. Arabnia, Proceedings of the 2016 International Conference on Health Informatics and Medical Systems (HIMS'15), July 27-30, Las Vegas, Nevada, USA, pp. 163-164, 2016.
  3. "Obstacle Detection and Avoidance for the Visually Impaired in Indoors Environments Using Google’s Project Tango Device", Rabia Jafri and Marwa Mahmoud Khan, Computers Helping People with Special Needs: 15th International Conference, ICCHP 2016, Linz, Austria, July 13-15, 2016, Springer International Publishing, Lecture Notes in Computer Science (LNCS), Volume 9759, pp. 179-185, 2016.

Thesis

Masterthesis

  1. Steffen Troster: Optimization of Augmented Reality Applications considering the Depth Information with Google Project Tango. All the material related to it can be found here.
Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License