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2019 | OriginalPaper | Chapter

6D Pose Estimation for Industrial Applications

Authors : Federico Cunico, Marco Carletti, Marco Cristani, Fabio Masci, Davide Conigliaro

Published in: New Trends in Image Analysis and Processing – ICIAP 2019

Publisher: Springer International Publishing

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Abstract

Object pose estimation is important for systems and robots to interact with the environment where the main challenge of this task is the complexity of the scene caused by occlusions and clutters. A key challenge is performing pose estimation leveraging on both RGB and depth information: prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. Traditionally, the pose estimation problem is tackled by matching feature points between 3D models and images. However, these methods require rich textured models. In recent years, the raising of deep learning has offered an increasing number of methods based on neural networks, such as DSAC++, PoseCNN, DenseFusion and SingleShotPose. In this work, we present a comparison between two recent algorithms, DSAC++ and DenseFusion, focusing on computational cost, performance and applicability in the industry.

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Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32CrossRef

Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: Learning 6D object pose estimation using 3D object coordinates. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 536–551. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_35CrossRef

Brachmann, E., et al.: DSAC - differentiable RANSAC for camera localization. In: CVPR (2017)

Brachmann, E., Rother, C.: 6D camera localization via 3D surface regression. In: CVPR (2018)

Choi, C., Christensenb, H.I.: 3D textureless object detection and tracking: an edge-based approach. In: IROS (2012)

Choi, C., Christensenb, H.I.: RGB-D object pose estimation in unstructured environments. RAS 75, 595–613 (2016)

Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)MathSciNetCrossRef

Gao, X., Hou, X., Tang, J., Cheng, H.: Complete solution classification for the perspective-three-point problem. TPAMI 25, 930–943 (2003)CrossRef

Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The Kitti vision benchmark suite. In: CVPR (2012)

10.

Glocker, B., Izadi, S., Shotton, J., Criminisi, A.: Real-time RGB-D camera relocalization. In: ISMAR (2013)

11.

Hinterstoisser, S., et al.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: ICCV (2011)

12.

Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_42CrossRef

13.

Kehl, W., Milletari, F., Tombari, F., Ilic, S., Navab, N.: Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 205–220. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_13CrossRef

14.

Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DOF camera relocalization. In: ICCV (2015)

15.

Lee, K.: Augmented reality in education and training. TechTrends 56, 13–21 (2012)CrossRef

16.

Lee, Y.H., Medioni, G.: Wearable RGBD indoor navigation system for the blind. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8927, pp. 493–508. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16199-0_35CrossRef

17.

Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. IJCV 81, 155 (2009)CrossRef

18.

Li, C., Bai, J., Hager, G.D.: A unified framework for multi-view multi-class object pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 263–281. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_16CrossRef

19.

Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)

20.

Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. TVCG 22, 2633–2651 (2015)

21.

Marder-Eppstein, E.: Project tango. In: SIGGRAPH (2016)

22.

Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: CVPR (2015)

23.

Pavlakos, G., Zhou, X., Chan, A., Derpanis, K.G., Daniilidis, K.: 6-DOF object pose from semantic keypoints. In: ICRA (2017)

24.

Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2017)

25.

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR (2016)

26.

Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: CVPR (2017)

27.

Schwarz, M., Schulz, H., Behnke, S.: RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. In: ICRA (2015)

28.

Song, S., Xiao, J.: Sliding shapes for 3D object detection in depth images. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 634–651. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_41CrossRef

29.

Song, S., Xiao, J.: Deep sliding shapes for amodal 3D object detection in RGB-D images. In: CVPR (2016)

30.

Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6D object pose prediction. In: CVPR (2018)

31.

Wang, C., et al.: DenseFusion: 6D object pose estimation by iterative dense fusion. CoRR abs/1901.04780 (2019)

32.

Webel, S., Bockholt, U., Engelke, T., Gavish, N., Olbrich, M., Preusche, C.: An augmented reality training platform for assembly and maintenance skills. RAS 61, 398–403 (2013)

33.

Weiss, S., Achtelik, M.W., Chli, M., Siegwart, R.: Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV. In: ICRA (2012)

34.

Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes. RSS (2018)

35.

Zhu, M., et al.: Single image 3D object detection and pose estimation for grasping. In: ICRA (2014)

Title: 6D Pose Estimation for Industrial Applications
Authors: Federico Cunico
Marco Carletti
Marco Cristani
Fabio Masci
Davide Conigliaro
Publisher: Springer International Publishing
Book: New Trends in Image Analysis and Processing – ICIAP 2019
Print ISBN: 978-3-030-30753-0

Electronic ISBN: 978-3-030-30754-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30754-7_37

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