Custom Dataset Loader and Transformations (W2C, C2W)

[robofar]

Hello. I want to write custom dataset loader. Dataset gives position and orientation of camera (in world frame).

I just want to know, for R and T, should I save w2c transformation or c2w transformation. When I say c2w I mean, transformation that is constructed directly from the position and orientation of camera/vehicle given by dataset.

I am asking, because I was trying something, but it did not work. So I want to confirm. Another question, for method getWorld2View2 why you are transposing rotation matrix?

def getWorld2View2(R, t, translate=torch.tensor([0.0, 0.0, 0.0]), scale=1.0):
    translate = translate.to(R.device)
    Rt = torch.zeros((4, 4), device=R.device)

    Rt[:3, :3] = R.transpose(0,1) # why transposed here?
    #Rt[:3, :3] = R # Why not only R?
    Rt[:3, 3] = t
    Rt[3, 3] = 1.0

    C2W = np.linalg.inv(Rt) # so this is correct notation C2W
    cam_center = C2W[:3, 3]
    cam_center = (cam_center + translate) * scale
    C2W[:3, 3] = cam_center
    Rt = np.linalg.inv(C2W)
    return Rt

[turandai]

Hi, different datasets save different format of camera poses, you can choose whatever convenient to you. Here we follow the original pose definition in 3DGS for the Camera class, where R=c2w[:3, :3] and T=w2c[:3, 3]. So in your mentioned getWorld2View2, the R should be transposed to construct Rt=w2c.