Semantics-aware transformer for 3D reconstruction from binocular images

Xin JIA; Shourui YANG; Diyi GUAN

doi:10.1007/s11801-022-2055-0

[1] ZHANG Z, XUE W L. Video image mosaic via multi-module cooperation[J]. Optoelectronics letters, 2021, 17(11):688-692.

[2] ZHANG Z B, XUE W L, FU G K. Unsupervised image-to-image translation by semantics consistency and self-attention[J]. Optoelectronics letters, 2022, 18: 175-180.

[3] GAN Y, ZHANG J H, CHEN K Q, et al. A dynamic detection method to improve SLAM performance[J]. Optoelectronics letters, 2021, 17 (11):693-698.

[4] CHEN Z Q, ZHANG H. Learning implicit fields for generative shape modeling[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-20, 2019, Long Beach, California, USA. New York:IEEE, 2019:5939-5948.

[5] LIU F, TRAN L, LIU X. Fully understanding generic objects: modeling, segmentation, and reconstruction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021, Online, USA. New York:IEEE, 2021: 7423-7433.

[6] AGARWAL N, GOPI M. GAMesh:guided and augmented meshing for deep point networks[C]//Proceedings of the IEEE International Conference on 3D Vision (3DV), November 25-28, 2020, Online, USA. New York:IEEE, 2020:702-711.

[7] FAN H, SU H, GUIBAS L J. A point set generation network for 3D object reconstruction from a single image[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, Hawaii, USA. New York:IEEE, 2017:605-613.

[8] WANG N Y, ZHANG Y D, LI Z W, et al. Pixel2Mesh: 3D mesh model generation via image guided deformation[J]. IEEE transactions on pattern analysis and machine intelligence (TPAMI), 2021, 43(10):3600-3613.

[9] CHOY C B, XU D F, GWAK J Y, et al. 3D-R2N2:a unified approach for single and multi-view 3D object reconstruction[C]//European Conference on Computer Vision (ECCV), October 8-16, 2016, Amsterdam, the Netherlands. Heidelberg:Springer, 2016:628-644.

[10] CHEN R, HAN S F, XU J, et al. Point-based multi-view stereo network[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea. New York:IEEE, 2019:1538-1547.

[11] YAO Y, LUO Z X, LI S W, et al. Recurrent MVSNet for high-resolution multi-view stereo depth inference[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-20, 2019, Long Beach, California, USA. New York:IEEE, 2019:5525-5534.

[12] KAR A, HANE C, MALIK J. Learning a multi-view stereo machine[C]//Advances in Neural Information Processing Systems (NeurIPS), December 8-14, 2017, Long Beach, California, USA. Cambridge:MIT Press, 2017:365-376.

[13] WEN C, ZHANG Y D, LI Z W, et al. Pixel2Mesh++: multi-view 3D mesh generation via deformation[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea. New York:IEEE, 2019:1042-1051.

[14] JIA X, YANG S R, PENG Y X, et al. DV-net:dual-view network for 3D reconstruction by fusing multiple sets of gated control point clouds[J]. Pattern recognition letters, 2020, 131:376-382.

[15] ASHISH V, NOAM S, NIKI P, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems (NeurIPS), December 8-14, 2017, Long Beach, CA, USA. Cambridge:MIT Press, 2017:5998-6008.

[16] QI C R, YI L, SU H, et al. PointNet++:deep hierarchical feature learning on point sets in a metric space[C]//Advances in Neural Information Processing Systems (NeurIPS), December 8-14, 2017, Long Beach, California, USA. Cambridge: MIT Press, 2017: 5099-5108.

[17] HUANG L, WANG W M, CHEN J, et al. Attention on attention for image captioning[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 27- November 2, 2019, Seoul, Korea. New York:IEEE, 2019:4634-4643.

[18] YUAN W T, KHOT T, HELD D, et al. PCN:point completion network[C]//Proceedings of the IEEE International Conference on 3D Vision (3DV), September 5-8, 2018, Verona, Italy. New York:IEEE, 2020: 728-737.

[19] CHANG A X, FUNKHOUSER T, GUIBAS L, et al. Shapenet:an information-rich 3D model repository[EB/OL]. (2015-12-09) [2022-01-22]. http://arxiv.org/abs/1512.03012.

[20] KLOKOV R, BOYER E, VERBEEK J. Discrete point flow networks for efficient point cloud generation[C]//European Conference on Computer Vision (ECCV), August 23-28, 2020, Online. Heidelberg: Springer, 2020:694-710.

[21] MESCHEDER L, OECHSLE M, Niemeyer M, et al. Occupancy networks:learning 3D reconstruction in function space[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-20, 2019, Long Beach, California, USA. New York:IEEE, 2019:4460-4470.

[22] YAO Y, SCHERTLER N, ROSALES E, et al. Front2back:single view 3D shape reconstruction via front to back prediction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, Western Australia. New York:IEEE, 2020:531-540.