get a sphere noise point around the target

[Yes-Jumby]

i use the default params ,preprocessed data using colmap script by you and train on my data,but get this strenge results:the target is good except the points around.

[Yes-Jumby]

I note i have choose interes area,maybe i make the interest area too small?If so,why other scene works well? any advice?

[Totoro97]

Hello, yes the strange sphere looks outside the region of interest. I have just done a hack to this issue - could you try this extract_fields function below (to replace the original extract_fields function at L10 of models/renderer.py) and re-extract the mesh from your trained model again? Thanks!

def extract_fields(bound_min, bound_max, resolution, query_func):
    N = 64
    X = torch.linspace(bound_min[0], bound_max[0], resolution).split(N)
    Y = torch.linspace(bound_min[1], bound_max[1], resolution).split(N)
    Z = torch.linspace(bound_min[2], bound_max[2], resolution).split(N)

    u = np.zeros([resolution, resolution, resolution], dtype=np.float32)
    with torch.no_grad():
        for xi, xs in enumerate(X):
            for yi, ys in enumerate(Y):
                for zi, zs in enumerate(Z):
                    xx, yy, zz = torch.meshgrid(xs, ys, zs)
                    pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1)
                    dis = torch.linalg.norm(pts, ord=2, dim=-1, keepdim=False)
                    val = query_func(pts).reshape(-1)
                    val[torch.where(dis > 1.0)] = -1.0
                    val = val.reshape(len(xs), len(ys), len(zs)).detach().cpu().numpy()
                    u[xi * N: xi * N + len(xs), yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val
    return u

[Yes-Jumby]

i replace the function:answer is :reduce noise in the top ,but still have the noise
here is the reoutput ply file.
https://drive.google.com/file/d/1Ap3h6IBH1LbhuhMyuJ7vzN0MlXbItsbG/view?usp=sharing

Hello, yes the strange sphere looks outside the region of interest. I have just done a hack to this issue - could you try this extract_fields function below (to replace the original extract_fields function at L10 of models/renderer.py) and re-extract the mesh from your trained model again? Thanks!

def extract_fields(bound_min, bound_max, resolution, query_func):
    N = 64
    X = torch.linspace(bound_min[0], bound_max[0], resolution).split(N)
    Y = torch.linspace(bound_min[1], bound_max[1], resolution).split(N)
    Z = torch.linspace(bound_min[2], bound_max[2], resolution).split(N)

    u = np.zeros([resolution, resolution, resolution], dtype=np.float32)
    with torch.no_grad():
        for xi, xs in enumerate(X):
            for yi, ys in enumerate(Y):
                for zi, zs in enumerate(Z):
                    xx, yy, zz = torch.meshgrid(xs, ys, zs)
                    pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1)
                    dis = torch.linalg.norm(pts, ord=2, dim=-1, keepdim=False)
                    val = query_func(pts).reshape(-1)
                    val[torch.where(dis > 1.0)] = -1.0
                    val = val.reshape(len(xs), len(ys), len(zs)).detach().cpu().numpy()
                    u[xi * N: xi * N + len(xs), yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val
    return u

[Yes-Jumby]

here is the validate_image,it is perfect .