Bergman

References

[1] B. Schwarz, “LIDAR: Mapping the world in 3D,” Nature Photonics, vol. 4, pp. 429–430, 2010. [2] U. Weiss and P. Biber, “Plant detection and mapping for agricultural robots using a 3D LIDAR sensor,” Robotics and Autonomous Systems, vol. 59, pp. 265–273, 2011. [3] A. M. Pawlikowska, A. Halimi, R. A. Lamb, and G. S. Buller, “Single-photon three-dimensional imaging at up to 10 kilometers range,” Optics Express, vol. 25, no. 10, pp. 11 919–11 931, 2017. [4] D. B. Lindell, M. OToole, and G. Wetzstein, “Single-Photon 3D Imaging with Deep Sensor Fusion,” ACM Trans. Graph. (SIG548 GRAPH), no. 4, 2018. [5] F. Ma and S. Karaman, “Sparse-to-dense: Depth prediction from sparse depth samples and a single image,” ICRA, 2018. [6] A. Eldesokey, M. Felsberg, and F. Khan, “Confidence propagation through cnns for guided sparse depth regression,” IEEE PAMI, vol. PP, pp. 1–1, 07 2019 [1] F. Ma, G. V. Cavalheiro, and S. Karaman, “Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera,” ICRA, 2019. [1] M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” in Advances in Neural Information Processing Systems, 2015.

Appendix

This work is a collaboration with David B. Lindell and Gordon Wetzstein of the Stanford Computational Imaging Group. The team member contribution is broken down as follows:

Alexander W. Bergman (PSYCH 221 student) performed the literature review and analysis, helped brainstorm the solution method, implemented the methods and collected the results, formulated the conclusion drawn from the results, and wrote the report.

David B. Lindell (non-PSYCH 221 student) proposed the idea for the project, helped with the literature review, provided guidance and suggestions on the methods, helped speculate on the interpretation of the results.

Gordon Wetzstein (non-PSYCH 221 student) defined the motivation for the project and desired results to pursue, helped with the literature review, provided guidance and suggestion on the methods, and provided the computing resources for developing the project.

I would like to thank my collaborators for their suggestions and guidance in my development of this project - without their input this project would not be where it is now.

Source Code

The repository containing the source code for the methods and evaluation of this project is available upon request. Contact awb@stanford.edu.

Useful: You can write math equations as follows: $y=x+5$

You can include images as follows (you will need to upload the image first using the toolbox on the left bar, using the "Upload file" link).