374
V. L. Kondarattsev et al.
understand the geometric structure of the surrounding space and its relationships to
semantic information about objects.
References
1. Zhang, K., Hao, M., Wang, J., de Silva, C.W., Fu, C.: Linked dynamic graph CNN: learning on
point cloud via linking hierarchical features. CoRR ArXiv Preprint, arXiv:1904.10014 [cs.CV]
(2019)
2. Vranic, D.V.: 3D model retrieval. Ph. D. Dissertation, University of Leipzig (2004)
3. Weinmann, M., Jutzi, B., Hinz, S., Mallet, C.: Semantic point cloud interpretation based
on optimal neighborhoods, relevant features and efficient classifiers. ISPRS J. Photogramm.
Remote Sens. 105, 286–304 (2015)
4. Zhang, J., Zhao, X., Chen, Z., Lu, Z.: A review of deep learning-based semantic segmentation
for point cloud. IEEE Access 7, 179118–179133 (2019)
5. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D ShapeNets: a deep
representation for volumetric shapes. In: IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, pp. 1912–1920. IEEE Computer Society Press, Boston, MA,
USA (2015)
6. Armeni. I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3D semantic
parsing of large-scale indoor spaces. In: IEEE Computer Society Conference on Computer
Vision and Pattern Recognit., pp. 1534–1543. IEEE Computer Society Press, Las Vegas, NV,
USA (2016)
7. Yi, L., Shao, L., Savva, M., Huang, H., Zhou, Y., Wang, Q., Graham, B., Engelcke, M., Klokov,
R., Lempitsky, V., Gan, Y., Wang, P., Liu, K., Yu, F., Shui, P., Hu, B., Zhang, Y., Li, Y., Bu,
R., Sun, M., Wu, W., Jeong, M., Choi, J., Kim, C., Geetchandra, A., Murthy, N., Ramu, B.,
Manda, B., Ramanathan, M., Kumar, G., Preetham, P., Srivastava, S., Bhugra, S., Lall, B.,
Haene, C., Tulsiani, S., Malik, J., Lafer, J., Jones, R., Li, S., Lu, J., Jin, S., Yu, J., Huang, Q.,
Kalogerakis, E., Savarese, S., Hanrahan, P., Funkhouser, T., Su, H., Guibas, L.: Large-scale 3D
shape reconstruction and segmentation from ShapeNet Core55. CoRR ArXiv Preprint, arXiv:
1710.06104 [cs.CV] (2017)
8. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: Richlyannotated 3D reconstructions of indoor scenes. In: IEEE Conference on Computer Vision and
Pattern Recognition, pp. 2432–2443. IEEE Computer Society Press, Honolulu, HI, USA (2017)
9. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph Cnn
for learning on point clouds. ACM Trans. Graph. 38(5), 146.1–146.13 (2019)
10. Reingold, O.: Undirected connectivity in log-space. J. ACM. 55, 1–24 (2008)
11. Cohen, M.B., Lee, Y.T., Miller, G., Pachocki, J., Sidford, A.: Geometric median in nearly linear
time. In: The 48th Annual ACM SIGACT Symposium on Theory of Computing—STOC 2016,
pp. 9–21. ACM Press, New York, New York, USA (2016)
12. GitHub page of our project. https://github.com/phygitalism/blender_scan_filter. Last accessed
2020/06/26
13. Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. CoRR
ArXiv Preprint, arXiv:1801.09847 [cs.CV] (2018)
14. GitHub page of LDGCNN. https://github.com/KuangenZhang/ldgcnn. Last accessed
2020/06/26
15. Kelleher, J.D., Namee, B.M., D’Arcy, A.: Fundamentals of Machine Learning for Predictive
Data Analytics Algorithms: Worked examples, and Case Studies. The MIT Press, London
(2015)
16. Novotni, M., Klein, R.: 3D Zernike descriptors for content based shape retrieval. In: Symposium
on Solid Modeling and Applications, pp. 216–225. Washington, USA (2003)
17. Ede, J.D., Wenninger, M.J.: Spherical models. Math. Gaz. 65(431), 65 (1981)
V. L. Kondarattsev et al.
understand the geometric structure of the surrounding space and its relationships to
semantic information about objects.
References
1. Zhang, K., Hao, M., Wang, J., de Silva, C.W., Fu, C.: Linked dynamic graph CNN: learning on
point cloud via linking hierarchical features. CoRR ArXiv Preprint, arXiv:1904.10014 [cs.CV]
(2019)
2. Vranic, D.V.: 3D model retrieval. Ph. D. Dissertation, University of Leipzig (2004)
3. Weinmann, M., Jutzi, B., Hinz, S., Mallet, C.: Semantic point cloud interpretation based
on optimal neighborhoods, relevant features and efficient classifiers. ISPRS J. Photogramm.
Remote Sens. 105, 286–304 (2015)
4. Zhang, J., Zhao, X., Chen, Z., Lu, Z.: A review of deep learning-based semantic segmentation
for point cloud. IEEE Access 7, 179118–179133 (2019)
5. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D ShapeNets: a deep
representation for volumetric shapes. In: IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, pp. 1912–1920. IEEE Computer Society Press, Boston, MA,
USA (2015)
6. Armeni. I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3D semantic
parsing of large-scale indoor spaces. In: IEEE Computer Society Conference on Computer
Vision and Pattern Recognit., pp. 1534–1543. IEEE Computer Society Press, Las Vegas, NV,
USA (2016)
7. Yi, L., Shao, L., Savva, M., Huang, H., Zhou, Y., Wang, Q., Graham, B., Engelcke, M., Klokov,
R., Lempitsky, V., Gan, Y., Wang, P., Liu, K., Yu, F., Shui, P., Hu, B., Zhang, Y., Li, Y., Bu,
R., Sun, M., Wu, W., Jeong, M., Choi, J., Kim, C., Geetchandra, A., Murthy, N., Ramu, B.,
Manda, B., Ramanathan, M., Kumar, G., Preetham, P., Srivastava, S., Bhugra, S., Lall, B.,
Haene, C., Tulsiani, S., Malik, J., Lafer, J., Jones, R., Li, S., Lu, J., Jin, S., Yu, J., Huang, Q.,
Kalogerakis, E., Savarese, S., Hanrahan, P., Funkhouser, T., Su, H., Guibas, L.: Large-scale 3D
shape reconstruction and segmentation from ShapeNet Core55. CoRR ArXiv Preprint, arXiv:
1710.06104 [cs.CV] (2017)
8. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: Richlyannotated 3D reconstructions of indoor scenes. In: IEEE Conference on Computer Vision and
Pattern Recognition, pp. 2432–2443. IEEE Computer Society Press, Honolulu, HI, USA (2017)
9. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph Cnn
for learning on point clouds. ACM Trans. Graph. 38(5), 146.1–146.13 (2019)
10. Reingold, O.: Undirected connectivity in log-space. J. ACM. 55, 1–24 (2008)
11. Cohen, M.B., Lee, Y.T., Miller, G., Pachocki, J., Sidford, A.: Geometric median in nearly linear
time. In: The 48th Annual ACM SIGACT Symposium on Theory of Computing—STOC 2016,
pp. 9–21. ACM Press, New York, New York, USA (2016)
12. GitHub page of our project. https://github.com/phygitalism/blender_scan_filter. Last accessed
2020/06/26
13. Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. CoRR
ArXiv Preprint, arXiv:1801.09847 [cs.CV] (2018)
14. GitHub page of LDGCNN. https://github.com/KuangenZhang/ldgcnn. Last accessed
2020/06/26
15. Kelleher, J.D., Namee, B.M., D’Arcy, A.: Fundamentals of Machine Learning for Predictive
Data Analytics Algorithms: Worked examples, and Case Studies. The MIT Press, London
(2015)
16. Novotni, M., Klein, R.: 3D Zernike descriptors for content based shape retrieval. In: Symposium
on Solid Modeling and Applications, pp. 216–225. Washington, USA (2003)
17. Ede, J.D., Wenninger, M.J.: Spherical models. Math. Gaz. 65(431), 65 (1981)