Street-view change detection with deconvolutional networks

Robotics: Science and Systems

Volumn 12, Issue , 2016, Pages

  Alcantarilla, Pablo F ^a   Stent, Simon ^b   Ros, German ^c   Arroyo, Roberto ^d   Gherardi, Riccardo ^e  

^a IRobot Corporation   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c Barcelona Perception Computing Lab   (Spain)

^d UNIVERSITY OF ALCALÁ   (Spain)

^e TOSHIBA CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

AIR NAVIGATION; ROBOTS;

3D RECONSTRUCTION; AUTONOMOUS VEHICLE NAVIGATION; LIGHTING VARIATIONS; MONOCULAR CAMERAS; MULTI-SENSOR FUSION; REGISTERED IMAGES; STRUCTURAL CHANGE DETECTIONS; URBAN CHANGE DETECTION;

ROBOTICS;

EID: 85044678612     PISSN: None     EISSN: 2330765X     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (42)

1. P. F. Alcantarilla, J. Nuevo, and A. Bartoli. Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. In British Machine Vision Conf. (BMVC), 2013.
2. H. Badino, D. Huber, and T. Kanade. Visual Topometric Localization. In IEEE Intelligent Vehicles Symposium (IV), 2011.
3. H. Badino, D. Huber, and T. Kanade. Real-Time Topometric Localization. In IEEE Intl. Conf. on Robotics and Automation (ICRA), 2012.
4. N. Carlevaris-Blanco and R. M. Eustice. Learning Visual Feature Descriptors for Dynamic Lighting Conditions. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2014.
5. G. Chaurasia, S. Duchene, O. Sorkine-Hornung, and G. Drettakis. Depth Synthesis and Local Warps for Plausible Image-based Navigation. ACM Transactions on Graphics, 32, 2013.
6. M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, and L. Van Gool. SEEDS: Superpixels Extracted via Energy-Driven Sampling. In Eur. Conf. on Computer Vision (ECCV), 2012.
7. R. Finman, T. Whelan, M. Kaess, and J. J. Leonard. Toward Lifelong Object Segmentation from Change Detection in Dense RGB-D Maps. In European Conf. on Mobile Robots (ECMR), 2013.
8. Y. Furukawa and J. Ponce. Accurate, dense, and robust multi-view stereopsis. IEEE Trans. Pattern Anal. Machine Intell., 32(8):1362-1376, 2010.
9. A. Geiger, M. Roser, and R. Urtasun. Efficient Large-Scale Stereo Matching. In Asian Conf. on Computer Vision (ACCV), pages 25-38, 2010.
10. X. Glorot, A. Bordes, and Y. Bengio. Deep Sparse Rectifier Neural Networks. In Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), pages 315-323, 2011.
11. M. Goesele, N. Snavely, B. Curless, H. Hoppe, and S.M. Seitz. Multi-View Stereo for Community Photo Collections. In Intl. Conf. on Computer Vision (ICCV), pages 1-8, 2007.
12. N. Goyette, P-M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar. Changedetection. net: A new change detection benchmark dataset. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012.
13. R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, second edition, 2004.
14. K. He, X. Zhang, S. Ren, and J. Sun. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In Intl. Conf. on Computer Vision (ICCV), 2015.
15. V. Indelman, S. Williams, M. Kaess, and F. Dellaert. Information fusion in navigation systems via factor graph based incremental smoothing. Journal of Robotics and Autonomous Systems, 61(8):721-738, 2013.
16. S. Ioffe and C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Intl. Conf. on Machine Learning (ICML), 2015.
17. Z. Kang and G. Medioni. 3D Urban Reconstruction from Wide Area Aerial Surveillance Video. In Workshop on Applications for Aerial Video Exploitation (WAVE), 2015.
18. S. Kim, D. Min, B. Ham, S. Ryu, M. N. Do, and K. Sohn. DASC: Dense Adaptive Self-Correlation Descriptor for Multi-modal and Multi-spectral Correspondence. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015.
19. D. P. Kingma and J. Ba. Adam: A Method for Stochastic Optimization. In Intl. Conf. on Learning Representations (ICLR), 2015.
20. A. Krizhevsky, I. Sustkever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pages 1097-1105, 2012.
21. M.-Y. Liu, S. Lin, S. Ramalingam, and O. Tuzel. Layered Interpretation of Street View Images. In Robotics: Science and Systems (RSS), 2015.
22. J. Long, E. Shelhamer, and T. Darrell. Fully Convolutional Networks for Semantic Segmentation. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015.
23. D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. Intl. J. of Computer Vision, 60(2): 91-110, 2004.
24. R. Martin-Brualla, D. Gallup, and S. M. Seitz. Timelapse mining from internet photos. ACM Transactions on Graphics (TOG), 34(4):62, 2015.
25. K. Matzen and N. Snavely. Scene Chronology. In Eur. Conf. on Computer Vision (ECCV), 2014.
26. D. Min, S. Choi, J. Lu, B. Ham, K. Sohn, and M.N. Do. Fast global image smoothing based on weighted least squares. IEEE Trans. Image Processing, 23(12): 5638-53, 2014.
27. E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd. Generic and real-time structure from motion using local bundle adjustment. 27:1178-1193, 2009.
28. H. Noh, S. Hong, and B. Han. Learning Deconvolution Network for Semantic Segmentation. In Intl. Conf. on Computer Vision (ICCV), 2015.
29. R. Qin and A. Gruen. 3D change detection at street level using mobile laser scanning point clouds and terrestrial images. ISPRS Journal of Photogrammetry and Remote Sensing, 90:23-35, 2014.
30. R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam. Image change detection algorithms: a systematic survey. IEEE Trans. Image Processing, 14(3):294-307, 2005.
31. G. Ros, S. Stent, P. F. Alcantarilla, and T. Watanabe. Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation. arXiv preprint arXiv:1604.01545, 2016.
32. K. Sakurada and T. Okatani. Change Detection from a Street Image Pair using CNN Features and Superpixel Segmentation. In British Machine Vision Conf. (BMVC), 2015.
33. K. Sakurada, T. Okatani, and K. Deguchi. Detecting Changes in 3D Structure of a Scene from Multi-view Images Captured by a Vehicle-mounted Camera. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 137-144, 2013.
34. G. Schindler and F. Dellaert. Probabilistic Temporal Inference on Reconstructed 3D Scenes. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010.
35. Q. Shan, B. Curless, Y. Furukawa, C. Hernandez, and S. M. Seitz. Occluding Contours for Multi-View Stereo. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014.
36. S. Stent, R. Gherardi, B. Stenger, and R. Cipolla. Detecting Change for Multi-View, Long-Term Surface Inspection. In British Machine Vision Conf. (BMVC), 2015.
37. N. Sünderhauf, S. Shirazi, A. Jacobson, D. Ferab, E. Pepperell, B. Upcroft, and M. Milford. Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free. In Robotics: Science and Systems (RSS), 2015.
38. A. Taneja, L. Ballan, and M. Pollefeys. Image Based Detection of Geometric Changes in Urban Environments. In Intl. Conf. on Computer Vision (ICCV), pages 2336-2343, 2011.
39. A. Taneja, L. Ballan, and M. Pollefeys. City-Scale Change Detection in Cadastral 3D Models using Images. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013.
40. E. Tola, V. Lepetit, and P. Fua. DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo . IEEE Trans. Pattern Anal. Machine Intell., 32(5):815-830, 2010.
41. A. O. Ulusoy and J. L. Mundy. Image-based 4-D reconstruction using 3-D change detection. In Eur. Conf. on Computer Vision (ECCV), pages 31-45, 2014.
42. A. Vedaldi and K. Lenc. MatConvNet-Convolutional Neural Networks for MATLAB. 2015.