Question about Multi-Camera Data Usage and Calibration in EasyVolcap

[YongmoonPark]

I've confirmed that using custom static multi-view data works well. However, I'm encountering difficulties with multi-camera data. I attempted to convert the calibration values of my 36-channel camera setup to the EasyVolcap calibration format, which resulted in unsatisfactory outcomes. I have several questions regarding this:

1. For multi-camera setups that cannot use Colmap, should I proceed with calibration using the EasyMocap method (EasyMocap calibration)? Is there a specific calibration technique recommended for EasyVolcap?

2. Looking at the sample data, the extrinsic calibration value for Rot_00 is not an identity matrix but something different.
   Our calibration is based on cam_00, which leads me to think that the relative positioning of the cameras might not significantly alter the outcome, depending on which reference point is used. However, given the unsatisfactory results, I am keeping all possibilities open. What reference point was used to determine the camera positions in the sample? Would using the EasyMocap method yield a Rot_00 value similar to the sample?

I'm looking for guidance on whether there's a specific calibration method required for EasyVolcap and how to address the calibration issues with multi-camera data effectively. Thank you for your assistance.

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my calibration data

R_000000: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [0.0000000000, 0.0000000000, 0.0000000000]
Rot_000000: !!opencv-matrix
  rows: 3
  cols: 3
  dt: d
  data: [1.0000000000, 0.0000000000, 0.0000000000, 0.0000000000, 1.0000000000, 0.0000000000, 0.0000000000, 0.0000000000, 1.0000000000]
T_000000: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [0.0000000000, 0.0000000000, 0.0000000000]
R_000001: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [-0.0387293496, 0.2812993123, 0.1085048680]
Rot_000001: !!opencv-matrix
  rows: 3
  cols: 3
  dt: d
  data: [0.9548976038, 0.1014360964, -0.2790721852, -0.1122470035, 0.9934143119, -0.0229916312, 0.2749021215, 0.0532796701, 0.9599948439]
T_000001: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [-0.9293841881, -0.1210860311, 0.0560820123]

---

0013_01's calibration data

R_00: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [1.6823949814, 1.4320055246, -0.6942056417]
Rot_00: !!opencv-matrix
  rows: 3
  cols: 3
  dt: d
  data: [0.2076084018, 0.9741442800, 0.0891154334, 0.5334432125, -0.0363806561, -0.8450531363, -0.8199616075, 0.2229781598, -0.5272035599]
T_00: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [0.1817326248, 0.0986888334, 3.2907896042]
t_00: 0.0000000000
n_00: 0.5000000000
f_00: 20.0000000000
bounds_00: !!opencv-matrix
  rows: 2
  cols: 3
  dt: d
  data: [-0.1252000183, -0.3997000754, -0.9367001057, 0.3047999740, 0.1002999693, 0.7732999325]
R_01: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [1.5115214586, 1.2331650257, -0.7963469625]
Rot_01: !!opencv-matrix
  rows: 3
  cols: 3
  dt: d
  data: [0.2666431665, 0.9592524171, 0.0934668258, 0.3094531894, 0.0066335150, -0.9508914948, -0.9127650261, 0.2824722826, -0.2950749993]
T_01: !!opencv-matrix
  rows: 3
  cols: 1
  dt: d
  data: [0.1018460914, -0.0497235581, 3.1227753162]
t_01: 0.0000000000
n_01: 0.5000000000
f_01: 20.0000000000
bounds_01: !!opencv-matrix
  rows: 2
  cols: 3
  dt: d
  data: [-0.1252000183, -0.3997000754, -0.9367001057, 0.3047999740, 0.1002999693, 0.7732999325]