Fetching the paper…
Reading the bibliography…
Learning Bird's Eye View (BEV) representation from surrounding-view cameras is of great importance for autonomous driving.
T. Roddick, A. Kendall, and R. Cipolla, “Orthographic feature transform for monocular 3d object detection,” in
2019
Earlier work this paper cites.
M. Tan and Q. V. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in
2019
Earlier work this paper cites.
J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in
2020
Earlier work this paper cites.
T. Roddick and R. Cipolla, “Predicting semantic map representations from images using pyramid occupancy networks,” in
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in
2020
Cited alongside, same era.
B. Pan, J. Sun, H. Y. T. Leung, A. Andonian, and B. Zhou, “Cross-view semantic segmentation for sensing surroundings,”
2020
Cited alongside, same era.
A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V. Badrinarayanan, R. Cipolla, and A. Kendall, “FIERY: future instance prediction in bird’s-eye view from surround monocular cameras,” in
2021
Cited alongside, same era.
2021
Cited alongside, same era.
A. Saha, O. M. Maldonado, C. Russell, and R. Bowden, “Enabling spatio-temporal aggregation in birds-eye-view vehicle estimation,” in
2021
Later among the works it cites.
2022
Closest in time.
B. Zhou and P. Krähenbühl, “Cross-view transformers for real-time map-view semantic segmentation,”
2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…