![](bg8.png)
bility with multi-view geometry. In Proc. IEEE Con-
ference on Computer Vision and Pattern Recognition
(CVPR).
Dong, X., Garratt, M. A., Anavatti, S. G., and Abbass, H. A.
(2021). Towards real-time monocular depth estima-
tion for robotics: A survey. IEEE Transactions on
Intelligent Transportation Systems, 23:16940–16961.
Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready
for autonomous driving? the kitti vision benchmark
suite. In Conference on Computer Vision and Pattern
Recognition (CVPR).
Guizilini, V., Ambrus, R., Chen, D., Zakharov, S., and
Gaidon, A. (2022). Multi-frame self-supervised depth
with transformers. In Proceedings of the International
Conference on Computer Vision and Pattern Recogni-
tion (CVPR).
Hartley, R. and Zisserman, A. (2003). Multiple View Geom-
etry in Computer Vision. Cambridge University Press,
New York, NY, USA, 2 edition.
Intel (2020a). Intel Realsense D455. https://www.
intelrealsense.com/depth-camera-d455/. [Online; ac-
cessed 20-November-2022].
Intel (2020b). Intel Realsense L515. https://www.
intelrealsense.com/lidar-camera-l515/. [Online; ac-
cessed 20-November-2022].
Janoch, A. (2012). The berkeley 3d object dataset. Mas-
ter’s thesis, EECS Department, University of Califor-
nia, Berkeley.
Li, S., Luo, Y., Zhu, Y., Zhao, X., Li, Y., and Shan,
Y. (2021). Enforcing temporal consistency in video
depth estimation. In Proceedings of the IEEE/CVF
International Conference on Computer Vision Work-
shops.
Li, Z., Wang, X., Liu, X., and Jiang, J. (2022). Binsformer:
Revisiting adaptive bins for monocular depth estima-
tion. arXiv preprint arXiv:2204.00987.
Livox (2021). Livox Avia. https://www.livoxtech.com/avia.
[Online; accessed 20-November-2022].
Microsoft (2020). Microsoft Azure Kinect DK. https://
azure.microsoft.com/en-us/products/kinect-dk/. [On-
line; accessed 20-November-2022].
Ming, Y., Meng, X., Fan, C., and Yu, H. (2021). Deep
learning for monocular depth estimation: A review.
Neurocomputing, 438:14–33.
Nathan Silberman, Derek Hoiem, P. K. and Fergus, R.
(2012). Indoor segmentation and support inference
from rgbd images. In ECCV.
Song, S., Lichtenberg, S. P., and Xiao, J. (2015). Sun
rgb-d: A rgb-d scene understanding benchmark suite.
2015 IEEE Conference on Computer Vision and Pat-
tern Recognition (CVPR), pages 567–576.
Sun, L., Bian, J.-W., Zhan, H., Yin, W., Reid, I.,
and Shen, C. (2022). Sc-depthv3: Robust self-
supervised monocular depth estimation for dynamic
scenes. arXiv:2211.03660.
Tychola, K., Tsimperidis, I., and Papakostas, G. (2022).
On 3d reconstruction using rgb-d cameras. Digital,
2:401–423.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones,
L., Gomez, A. N., Kaiser, L. u., and Polosukhin,
I. (2017). Attention is all you need. In Guyon,
I., Luxburg, U. V., Bengio, S., Wallach, H., Fer-
gus, R., Vishwanathan, S., and Garnett, R., editors,
Advances in Neural Information Processing Systems,
volume 30. Curran Associates, Inc.
Wang, H., Wang, C., and Xie, L. (2021). Lightweight 3-d
localization and mapping for solid-state lidar. IEEE
Robotics and Automation Letters, 6(2):1801–1807.
Xiao, J., Owens, A., and Torralba, A. (2013). Sun3d: A
database of big spaces reconstructed using sfm and
object labels. 2013 IEEE International Conference on
Computer Vision, pages 1625–1632.
Yuan, C., Xu, W., Liu, X., Hong, X., and Zhang, F. (2022a).
Efficient and probabilistic adaptive voxel mapping for
accurate online lidar odometry. IEEE Robotics and
Automation Letters, 7:8518–8525.
Yuan, W., Gu, X., Dai, Z., Zhu, S., and Tan, P. (2022b).
Newcrfs: Neural window fully-connected crfs for
monocular depth estimation. In Proceedings of the
IEEE Conference on Computer Vision and Pattern
Recognition.
ZED (2017). ZED Mini . https://www.stereolabs.com/
zed-mini/. [Online; accessed 20-November-2022].
Zhang, S., Zheng, L., and Tao, W. (2021). Survey and eval-
uation of rgb-d slam. IEEE Access, 9:21367–21387.
Zollh
¨
ofer, M., Stotko, P., G
¨
orlitz, A., Theobalt, C., Nießner,
M., Klein, R., and Kolb, A. (2018). State of the art
on 3d reconstruction with rgb-d cameras. Computer
Graphics Forum, 37.
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