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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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18.01.2021

Yaron Lipman (Weizmann Institute of Science) will give a talk in the TUM AI lecture series on Jan 21st, 3pm! Livestream

10.12.2020

Frank Dellaert (Georgia Tech) will give a talk in the TUM AI lecture series on Dec 17th, 4pm! Livestream

15.10.2020

Jon Barron (Google) will give a talk in the TUM AI lecture series on Oct 22nd, 9pm! Livestream

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

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research:vslam:stereo-dso [2020/05/08 17:50]
Rui Wang
research:vslam:stereo-dso [2020/08/21 02:41] (current)
Rui Wang
Line 8: Line 8:
 ===== Abstract ===== ===== Abstract =====
 ** Stereo DSO ** is a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, it integrates constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo. Real-time optimization is realized by sampling pixels uniformly from image regions with sufficient intensity gradient. Fixed-baseline stereo resolves scale drift. It also reduces the sensitivities to large optical flow and to rolling shutter effect which are known shortcomings of direct image alignment methods. Quantitative evaluation demonstrates that the proposed Stereo DSO outperforms existing state-of-the-art visual odometry methods both in terms of tracking accuracy and robustness. Moreover, our method delivers a more precise metric 3D reconstruction than previous dense/semi-dense direct approaches while providing a higher reconstruction density than feature-based methods. ** Stereo DSO ** is a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, it integrates constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo. Real-time optimization is realized by sampling pixels uniformly from image regions with sufficient intensity gradient. Fixed-baseline stereo resolves scale drift. It also reduces the sensitivities to large optical flow and to rolling shutter effect which are known shortcomings of direct image alignment methods. Quantitative evaluation demonstrates that the proposed Stereo DSO outperforms existing state-of-the-art visual odometry methods both in terms of tracking accuracy and robustness. Moreover, our method delivers a more precise metric 3D reconstruction than previous dense/semi-dense direct approaches while providing a higher reconstruction density than feature-based methods.
 +
 +===== Citation =====
 +If you find our work useful in your research, please consider citing:
 +<code>
 +@inproceedings{wang2017stereoDSO,
 +    title={Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras},
 +    author={R. Wang and M. Schw\"orer and D. Cremers},
 +    booktitle={International Conference on Computer Vision (ICCV)},
 +    year={2017},
 +    month={October},
 +    address={Venice, Italy}
 +}
 +</code>
  
 ===== Download ===== ===== Download =====

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
CVG Group DVL Group

News

18.01.2021

Yaron Lipman (Weizmann Institute of Science) will give a talk in the TUM AI lecture series on Jan 21st, 3pm! Livestream

10.12.2020

Frank Dellaert (Georgia Tech) will give a talk in the TUM AI lecture series on Dec 17th, 4pm! Livestream

15.10.2020

Jon Barron (Google) will give a talk in the TUM AI lecture series on Oct 22nd, 9pm! Livestream

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

More