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Zero
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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact [email protected]
#
import torch
def mse(img1, img2):
return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
def psnr(img1, img2):
mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
return 20 * torch.log10(1.0 / torch.sqrt(mse))
def masked_psnr(img1, img2, mask):
mse = ((((img1 - img2)) ** 2) * mask).sum() / (3. * mask.sum())
return 20 * torch.log10(1.0 / torch.sqrt(mse))
def accuracy_torch(gt_points, rec_points, gt_normals=None, rec_normals=None, batch_size=5000):
n_points = rec_points.shape[0]
all_distances = []
all_indices = []
for i in range(0, n_points, batch_size):
end_idx = min(i + batch_size, n_points)
batch_points = rec_points[i:end_idx]
distances = torch.cdist(batch_points, gt_points) # (batch_size, M)
batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,)
all_distances.append(batch_distances)
all_indices.append(batch_indices)
distances = torch.cat(all_distances)
indices = torch.cat(all_indices)
acc = torch.mean(distances)
acc_median = torch.median(distances)
if gt_normals is not None and rec_normals is not None:
normal_dot = torch.sum(gt_normals[indices] * rec_normals, dim=-1)
normal_dot = torch.abs(normal_dot)
return acc, acc_median, torch.mean(normal_dot), torch.median(normal_dot)
return acc, acc_median
def completion_torch(gt_points, rec_points, gt_normals=None, rec_normals=None, batch_size=5000):
n_points = gt_points.shape[0]
all_distances = []
all_indices = []
for i in range(0, n_points, batch_size):
end_idx = min(i + batch_size, n_points)
batch_points = gt_points[i:end_idx]
distances = torch.cdist(batch_points, rec_points) # (batch_size, M)
batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,)
all_distances.append(batch_distances)
all_indices.append(batch_indices)
distances = torch.cat(all_distances)
indices = torch.cat(all_indices)
comp = torch.mean(distances)
comp_median = torch.median(distances)
if gt_normals is not None and rec_normals is not None:
normal_dot = torch.sum(gt_normals * rec_normals[indices], dim=-1)
normal_dot = torch.abs(normal_dot)
return comp, comp_median, torch.mean(normal_dot), torch.median(normal_dot)
return comp, comp_median
def accuracy_per_point(gt_points, rec_points, batch_size=5000):
n_points = rec_points.shape[0]
all_distances = []
all_indices = []
for i in range(0, n_points, batch_size):
end_idx = min(i + batch_size, n_points)
batch_points = rec_points[i:end_idx]
distances = torch.cdist(batch_points, gt_points) # (batch_size, M)
batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,)
all_distances.append(batch_distances)
all_indices.append(batch_indices)
distances = torch.cat(all_distances)
return distances
def completion_per_point(gt_points, rec_points, batch_size=5000):
n_points = gt_points.shape[0]
all_distances = []
all_indices = []
for i in range(0, n_points, batch_size):
end_idx = min(i + batch_size, n_points)
batch_points = gt_points[i:end_idx]
distances = torch.cdist(batch_points, rec_points) # (batch_size, M)
batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,)
all_distances.append(batch_distances)
all_indices.append(batch_indices)
distances = torch.cat(all_distances)
return distances |