DOI: 10.5061/dryad.6t1g1jxcm
title: A large-scale coherent 4D imaging sensor

description: Detailed and accurate three-dimensional (3D) mapping of dynamic
 environments is essential for machines to interface with their
 surroundings, and for human-machine interaction. Although considerable
 effort has been spent to create the equivalent of the CMOS image sensor
 for the 3D world, scalable, high-performance, reliable solutions have
 proven elusive. Focal plane array (FPA) sensors using frequency modulated
 continuous wave (FMCW) light detection and ranging (LiDAR) have shown
 potential to meet all the requirements and also provide direct measurement
 of radial velocity as a fourth dimension (4D). Prior demonstrations, while
 promising, have not achieved the simultaneous scale and performance
 required by commercial applications. In this paper, we present a
 large-scale, coherent LiDAR FPA enabled by comprehensive chip-scale
 optoelectronic integration. A 4D imaging camera is built around the FPA
 and used to acquire point clouds. At the core is a 352x176 pixel
 two-dimensional FMCW LiDAR FPA comprising over 0.6 million photonic
 components, all integrated on-chip together with their associated
 electronics. This represents a five times increase in pixel count with
 respect to previous demonstrations. The pixel architecture combines the
 outbound and inbound optical path within the pixel in a monostatic
 configuration, together with coherent detectors and electronics. Frequency
 modulated light is directed sequentially to groups of pixels by in-plane
 thermo-optic switches with integrated electronics for driving and
 calibration. An integrated serial digital interface controls both optical
 switching and readout synchronously. Point clouds of objects ranging from
 4 to 65 meters with per-pixel integration time compatible with frame rates
 from 3 to 15 fps are shown. This result demonstrates the capabilities of
 FMCW LiDAR FPA sensors as enablers of ubiquitous, low cost, compact
 coherent 4D imaging cameras.
description: # A large-scale coherent 4D imaging sensor Dataset DOI:
 [10.5061/dryad.6t1g1jxcm](https://doi.org/10.5061/dryad.6t1g1jxcm) ##
 Description of the data and file structure All the data that was used to
 generate the figures in the publication is included in the attached
 archive file (.zip). There are multiple folders in the archive file. Name
 of the folders match the Figures from the associated manuscript. (i. e.
 data from Figure 3c is in the folder named "Fig_3c") In each
 folder, there are datasets (in .csv form) and Python scripts in Jupyter
 Notebook form (when necessary). When these scripts are run together with
 the corresponding data from the same directory, they generate the figures
 in the paper that match the folder name. Note: There is no data affiliated
 with Fig. 1 and Fig. 2 available. Below are the summary of the data from
 the main text figures existing in the archive. Folder "Fig_3a":
 .csv data together with Jupyter Notebook that generate the pointcloud in
 Fig 3a. The columns in the .csv are named as x_m, y_m, and z_m, they
 correspond to Z, X and Y axis in the plotted figure. Folder
 "Fig_3b4x": .csv data together with Jupyter Notebook that
 generate the pointcloud in Fig 3b. The columns in the .csv are named as
 x_m, y_m, and z_m, they correspond to Z, X and Y axis in the plotted
 figure. Folder "Fig_3c": .csv data together with Jupyter
 Notebook that generate the velocity pointcloud in Fig 3c. The columns in
 the .csv are named as x_m, y_m, z_m, vel_f_m; they correspond to Z, X, Y
 axis, and velocity of the points in the plotted figure. Folder
 "Fig_3def": Contains the pictures used in Fig. 3 d, e and f. No
 data available. Folder "Fig_4a": .csv data together with Jupyter
 Notebook that generate the histogram in Fig 4a. The column in the .csv is
 named as data; and it corresponds to the optical power at the pixels.
 Folder "Fig_4b": .csv data together with Jupyter Notebook that
 generate the histogram in Fig 4b. The column in the .csv is named as data;
 and it corresponds to the Kappa values. The fit is also generated in the
 Jupyter script. Folder "Fig_4c": .csv data together with Jupyter
 Notebook that generate the plot in Fig 4c. The column in the .csv is named
 as XX, ratio_, YY, YY_fit, optical_signal_power and popt. The
 'nan' values are omitted by the script, they exist since the
 lengths of the arrays are different. The fit was performed elsewhere, and
 here is the processed data that is used to generate the plots directly.
 Folder "Fig_4d": .csv data together with Jupyter Notebook that
 generate the pointcloud in Fig 4d. The columns in the .csv are named as
 x_m, y_m, and z_m, they correspond to Z, X and Y axis in the plotted
 figure. Folder "Fig_4e": Contains the pictures used in Fig. 4e.
 No data available. Folder "Fig_4fg": Two .csv data files
 together with Jupyter Notebook that generate the histograms in Fig 4f and
 4g. The columns in the .csv files correspond to the data (distance error
 and velocity error respectively) obtained from different targets: Retro,
 50 %, 10 %, 5 %. The folders that are names as 'FigExt_##' are
 also organized similarly. Each folder stores the data in the .csv form,
 and each folder contains one Jupyter notebook file that calls the data and
 generates the figures associated with the name of the folder.   ### Files
 and variables #### File: Figure_Data.zip **Description: The main archive
 file containing the subfolders for each Figure in the manuscript.** ##
 Code/software All the scripts are run with Python 3. All the scripts rely
 only on widely available free libraries. (Numpy, Panda, Matplotlib,
 Plotly) In each folder, there is one python notebook and there are
 corresponding datafiles in .csv format. The python script should generate
 the figure in the manuscript using the data attached to it.