Abstract
RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.
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References
Lysenkov, I., Eruhimov, V., Bradski, G.: Recognition and pose estimation of rigid transparent objects with a kinect sensor. In: Proc. RSS (2012)
Alt, N., Rives, P., Steinbach, E.G.: Reconstruction of transparent objects in unstructured scenes with a depth camera. In: Proc. ICIP, pp. 4131–4135. IEEE (2013)
Maimone, A., Fuchs, H.: Reducing interference between multiple structured light depth sensors using motion. In: Proc. VRW, pp. 51–54 (2012)
Asteriadis, S., Chatzitofis, A., Zarpalas, D., Alexiadis, D.S., Daras, P.: Estimating human motion from multiple kinect sensors. In: Proc. MIRAGE, pp. 3:1–3:6 (2013)
Hossny, M., Filippidis, D., Abdelrahman, W., Zhou, H., Fielding, M., Mullins, J., Wei, L., Creighton, D., Puri, V., Nahavandi, S.: Low cost multimodal facial recognition via kinect sensors. In: Proc. LWC (2012)
Biswas, K.K., Basu, S.: Gesture recognition using microsoft kinect. In: Proc. ICARA, pp. 100–103 (2011)
Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (slam): Part ii. IEEE Robotics & Automation Magazine 13(3), 108–117 (2006)
Rafibakhsh, N., Gong, J., Siddiqui, M., Gordon, C., Lee, H.: 86. In: Analysis of XBOX Kinect Sensor Data for Use on Construction Sites: Depth Accuracy and Sensor Interference Assessment, pp. 848–857 (2012)
Maimone, A., Fuchs, H.: Encumbrance-free telepresence system with real-time 3d capture and display using commodity depth cameras. In: Proc. ISMAR, pp. 137–146 (2011)
Camplani, M., Salgado, L.: Efficient spatio-temporal hole filling strategy for kinect depth maps. In: Proc. of the SPIE, vol. 8290, pp. 82900E-82900E-10 (2012)
Miao, D., Fu, J., Lu, Y., Li, S., Chen, C.W.: Texture-assisted kinect depth inpainting. In: Proc. ISCAS, pp. 604–607 (2012)
Butler, D.A., Izadi, S., Hilliges, O., Molyneaux, D., Hodges, S., Kim, D.: Shake‘n’sense: Reducing interference for overlapping structured light depth cameras. In: Proc. SIGCHI, pp. 1933–1936 (2012)
Schröder, Y., Scholz, A., Berger, K., Ruhl, K., Guthe, S., Magnor, M.: Multiple kinect studies. Technical Report 09-15, ICG (2011)
Chan, D.Y., Hsu, C.H.: Regular stereo matching improvement system based on kinect-supporting mechanism. Open Journal Applied Sciences, 22–26 (2013)
Somanath, G., Cohen, S., Price, B., Kambhamettu, C.: Stereo+kinect for high resolution stereo correspondences. In: Proc. 3DV (2013)
Chiu, W.-C., Blank, U., Fritz, M.: Improving the kinect by cross-modal stereo. In: Proc. BMVC, pp. 116.1–116.10 (2011)
Cyganek, B., Siebert, J.P.: An introduction to 3D Computer Vision - Techniques and Algorithms. Wiley (2009)
Hirschmueller, H.: Stereo processing by semi-global matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 328–341 (2008)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCV library. O’Reilly Media, Inc. (2008)
Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: An open-source Robot Operating System. In: ICRA Workshop on Open Source Software (2009)
Rusu, R.B., Cousins, S.: 3d is here: Point cloud library (pcl). In: Proc. ICRA (2011)
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Alhwarin, F., Ferrein, A., Scholl, I. (2014). IR Stereo Kinect: Improving Depth Images by Combining Structured Light with IR Stereo. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_33
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DOI: https://doi.org/10.1007/978-3-319-13560-1_33
Publisher Name: Springer, Cham
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