Camera intrinsic matrix (3 by 3 matrix) has a negative value and only values on main diagonal?

[sarimmehdi]

I recorded a simulation by moving around with my camera and viewing a bunch of static objects. The camera intrinsic parameters were recorded as camera_intrinsic and extrinsic parameters were recorded as translation and rotation inside ego of the capture JSON (please correct me if this is wrong).

Now, I am experimenting with some triangulation methods (localizing objects using their 2D bounding box and the camera's projection matrix). Below is how I set up everything to get the projection matrix:

import numpy as np
from scipy.spatial.transform import Rotation as Rot
int_mat = np.array([
capture.sensor.camera_intrinsic[0],
capture.sensor.camera_intrinsic[1],
capture.sensor.camera_intrinsic[2]
])
r = Rot.from_quat(capture.ego.rotation)
rot_mat = r.as_matrix()
t = np.array([capture.ego.translation]).transpose()
ext_mat = np.hstack((rot_mat, t))
proj_mat = int_mat @ ext_mat

When I input the projection matrix and 2d bounding box into my custom algorithm, I noticed the triangulation was well-off. It was then that I noticed the matrix of intrinsic parameters only had values on the main diagonal and one of them is actually negative. I come from a computer vision background but not a computer graphics one. Can anyone here guide me on how to convert the intrinsic matrix provided by Unity Perception into a normal one with fx, fy, cx, cy, and skew?

I looked at the 3D Ground Truth Bounding Boxes inside the Perception_Statistics notebook but the authors seem to be considering the intrinsic parameter matrix as the projection matrix (Isn't the projection matrix supposed to be 3 by 4 due to the matrix multiplication of the 3 by 3 intrinsic parameter matrix with the 3 by 4 extrinsic parameter matrix?).

Furthermore, I also had a look at a similar question but the accepted answer there seems to assume the projection matrix is already provided while one of the answers is trying to use the FOV even though I don't see it in the data I have collected through Unity Perception.

For reference, below are the two relevant parts of the capture JSON file I am using:

"sensor": {
        "sensor_id": "7fcdda27-3029-4bb9-83f3-9d3eac23a1a1",
        "ego_id": "6a1ebd8b-1417-49a0-befc-8892801a9aa3",
        "modality": "camera",
        "translation": [
          0.0,
          0.0,
          0.0
        ],
        "rotation": [
          0.0,
          0.0,
          0.0,
          1.00000012
        ],
        "camera_intrinsic": [
          [
            0.705989,
            0.0,
            0.0
          ],
          [
            0.0,
            1.73205078,
            0.0
          ],
          [
            0.0,
            0.0,
            -1.0006001
          ]
        ]
      },
      "ego": {
        "ego_id": "6a1ebd8b-1417-49a0-befc-8892801a9aa3",
        "translation": [
          2.32,
          1.203,
          2.378
        ],
        "rotation": [
          -0.0229570474,
          0.976061463,
          -0.173389584,
          -0.129279226
        ],
        "velocity": null,
        "acceleration": null
      }