Fetching the paper…
Reading the bibliography…
Autonomous driving systems require High-Definition (HD) semantic maps to navigate around urban roads.
The hungarian method for the assignment problem
Kuhn, H. W · 1955
Earlier work this paper cites.
An iterative procedure for the polygonal approximation of plane curves
Ramer, U · 1972
Earlier work this paper cites.
Inverse perspective mapping simplifies optical flow computation and obstacle detection
Mallot, H. A., Bülthoff, H. H., Little, J., and Bohrer, S · 1991
Earlier work this paper cites.
Computing discrete fréchet distance
Eiter, T. and Mannila, H · 1994
Earlier work this paper cites.
Computing the discrete fréchet distance in subquadratic time
Agarwal, P. K., Avraham, R. B., Kaplan, H., and Sharir, M · 2014
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
Earlier work this paper cites.
Enhancing road maps by parsing aerial images around the world
Mattyus, G., Wang, S., Fidler, S., and Urtasun, R · 2015
Earlier work this paper cites.
Holistic 3d scene understanding from a single geo-tagged image
Wang, S., Fidler, S., and Urtasun, R · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Hd maps: Fine-grained road segmentation by parsing ground and aerial images
Máttyus, G., Wang, S., Fidler, S., and Urtasun, R · 2016
Earlier work this paper cites.
Torontocity: Seeing the world with a million eyes
Wang, S., Bai, M., Mattyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., and Urtasun, R · 2016
Earlier work this paper cites.
Annotating object instances with a polygon-rnn
Castrejon, L., Kundu, K., Urtasun, R., and Fidler, S · 2017
Earlier work this paper cites.
A neural representation of sketch drawings
Ha, D. and Eck, D · 2017
Earlier work this paper cites.
Vpgnet: Vanishing point guided network for lane and road marking detection and recognition
Lee, S., Kim, J., Shin Yoon, J., Shin, S., Bailo, O., Kim, N., Lee, T.-H., Seok Hong, H., Han, S.-H., and So Kweon, I · 2017
Earlier work this paper cites.
Grass: Generative recursive autoencoders for shape structures
Li, J., Xu, K., Chaudhuri, S., Yumer, E., Zhang, H., and Guibas, L · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Efficient interactive annotation of segmentation datasets with polygon-rnn++
Acuna, D., Ling, H., Kar, A., and Fidler, S · 2018
Earlier work this paper cites.
Hierarchical recurrent attention networks for structured online maps
Homayounfar, N., Ma, W.-C., Lakshmikanth, S. K., and Urtasun, R · 2018
Earlier work this paper cites.
Fixing weight decay regularization in adam
Loshchilov, I. and Hutter, F · 2018
Earlier work this paper cites.
Towards end-to-end lane detection: an instance segmentation approach
Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., and Van Gool, L · 2018
Earlier work this paper cites.
Spatial as deep: Spatial cnn for traffic scene understanding
Pan, X., Shi, J., Luo, P., Wang, X., and Tang, X · 2018
Earlier work this paper cites.
Hdnet: Exploiting hd maps for 3d object detection
Yang, B., Liang, M., and Urtasun, R · 2018
Cited alongside, same era.
Argoverse: 3d tracking and forecasting with rich maps
Chang, M.-F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., et al · 2019
Cited alongside, same era.
Pointpillars: Fast encoders for object detection from point clouds
Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O · 2019
Cited alongside, same era.
Line-cnn: End-to-end traffic line detection with line proposal unit
Li, X., Li, J., Hu, X., and Yang, J · 2019
Cited alongside, same era.
Convolutional recurrent network for road boundary extraction
Liang, J., Homayounfar, N., Ma, W.-C., Wang, S., and Urtasun, R · 2019
Cited alongside, same era.
Monocular semantic occupancy grid mapping with convolutional variational encoder–decoder networks
End-to-end multi-view fusion for 3d object detection in lidar point clouds
Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T., Guo, J., Ngiam, J., and Vasudevan, V · 2020
Later among the works it cites.
Deformable detr: Deformable transformers for end-to-end object detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J · 2020
Later among the works it cites.
Lane graph estimation for scene understanding in urban driving
Zürn, J., Vertens, J., and Burgard, W · 2020
Later among the works it cites.
Structured bird’s-eye-view traffic scene understanding from onboard images
Can, Y. B., Liniger, A., Paudel, D. P., and Van Gool, L · 2021
Later among the works it cites.
Mp3: A unified model to map, perceive, predict and plan
Casas, S., Sadat, A., and Urtasun, R · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lu, C., van de Molengraft, M. J. G., and Dubbelman, G · 2019
Cited alongside, same era.
Structurenet: Hierarchical graph networks for 3d shape generation
Mo, K., Guerrero, P., Yi, L., Su, H., Wonka, P., Mitra, N., and Guibas, L. J · 2019
Cited alongside, same era.
End-to-end lane detection through differentiable least-squares fitting
Van Gansbeke, W., De Brabandere, B., Neven, D., Proesmans, M., and Van Gool, L · 2019
Cited alongside, same era.
Jointnet: A common neural network for road and building extraction
Zhang, Z. and Wang, Y · 2019
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O · 2020
Cited alongside, same era.
Understanding bird’s-eye view semantic hd-maps using an onboard monocular camera
Can, Y. B., Liniger, A., Unal, O., Paudel, D., and Van Gool, L · 2020
Cited alongside, same era.
End-to-end object detection with transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S · 2020
Cited alongside, same era.
Ganin, Y., Bartunov, S., Li, Y., Keller, E., and Saliceti, S · 2021
Later among the works it cites.
Hdmapnet: A local semantic map learning and evaluation framework
Li, Q., Wang, Y., Wang, Y., and Zhao, H · 2021
Later among the works it cites.
Multimodal motion prediction with stacked transformers
Liu, Y., Zhang, J., Fang, L., Jiang, Q., and Zhou, B · 2021
Later among the works it cites.
Hdmapgen: A hierarchical graph generative model of high definition maps
Mi, L., Zhao, H., Nash, C., Jin, X., Gao, J., Sun, C., Schmid, C., Shavit, N., Chai, Y., and Anguelov, D · 2021
Later among the works it cites.
Im2vec: Synthesizing vector graphics without vector supervision
Reddy, P., Gharbi, M., Lukac, M., and Mitra, N. J · 2021
Later among the works it cites.
Argoverse 2: Next generation datasets for self-driving perception and forecasting
Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., Khandelwal, S., Pan, B., Kumar, R., Hartnett, A., Pontes, J. K., Ramanan, D., Carr, P., and Hays, J · 2021
Later among the works it cites.
Line segment detection using transformers without edges
Xu, Y., Xu, W., Cheung, D., and Tu, Z · 2021
Later among the works it cites.
Canvasvae: Learning to generate vector graphic documents
Yamaguchi, K · 2021
Later among the works it cites.
Projecting your view attentively: Monocular road scene layout estimation via cross-view transformation
Yang, W., Li, Q., Liu, W., Yu, Y., Ma, Y., He, S., and Pan, J · 2021
Later among the works it cites.
Futr3d: A unified sensor fusion framework for 3d detection
Chen, X., Zhang, T., Wang, Y., Wang, Y., and Zhao, H · 2022
Closest in time.
Rethinking efficient lane detection via curve modeling
Feng, Z., Guo, S., Tan, X., Xu, K., Wang, M., and Ma, L · 2022
Closest in time.
Dn-detr: Accelerate detr training by introducing query denoising
Li, F., Zhang, H., Liu, S., Guo, J., Ni, L. M., and Zhang, L · 2022
Closest in time.
End-to-end line drawing vectorization
Liu, H., Li, C., Liu, X., and Wong, T.-T · 2022
Closest in time.
Detr3d: 3d object detection from multi-view images via 3d-to-2d queries
Wang, Y., Guizilini, V. C., Zhang, T., Wang, Y., Zhao, H., and Solomon, J · 2022
Closest in time.
Cross-view transformers for real-time map-view semantic segmentation
Zhou, B. and Krähenbühl, P · 2022
Closest in time.
Polyworld: Polygonal building extraction with graph neural networks in satellite images
Zorzi, S., Bazrafkan, S., Habenschuss, S., and Fraundorfer, F · 2022
Closest in time.