sergeyprokudin

import torch
import torch.nn as nn

def laplacian_smoothing_loss2d(Y_pred):
    C, H, W = Y_pred.shape[1], Y_pred.shape[2], Y_pred.shape[3]
    kernel = torch.tensor([[0, 1, 0], [1, -4, 1], [0, 1, 0]], device=device, dtype=torch.float32)
    kernel = kernel.view(1, 1, 3, 3).repeat(C, 1, 1, 1)
    Y_laplacian = nn.functional.conv2d(Y_pred, kernel, groups=C, padding=1)
    laplacian_loss = (Y_laplacian ** 2).mean()
    return laplacian_loss

sergeyprokudin

# Pymeshlab normal estimation (and optional Poisson)

# https://github.com/cnr-isti-vclab/PyMeshLab/blob/main/docs/filter_list.rst
# https://pymeshlab.readthedocs.io/en/0.2/tutorials/get_mesh_values.html

import pymeshlab

def get_normals_pymeshlab(x,  k=10, smoothiter=0):

  tmp_mesh_path = 'tmp.obj'

sergeyprokudin

"""
MIT License

Copyright (c) 2024 Sergey Prokudin sergey.prokudin@gmail.com

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

sergeyprokudin

# following
# https://github.com/facebookresearch/pytorch3d/issues/35
import torch
import torch.nn as nn

class PointsRendererDepth(nn.Module):
    """
    A class for rendering a batch of points. The class should
    be initialized with a rasterizer and compositor class which each have a forward
    function.

sergeyprokudin

import open3d as o3d
import numpy as np

def poisson_open3d(ply_path, depth=8):
  
  pcd = o3d.io.read_point_cloud(ply_path)
  mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=8)
  vertices_to_remove = densities < np.quantile(densities, 0.02)
  mesh.remove_vertices_by_mask(vertices_to_remove)

sergeyprokudin

sergeyprokudin

newmtl material_1
map_Kd rp_mei_posed_001_dif.jpg

sergeyprokudin

5.251361131668090820e-01 9.620525240898132324e-01 7.196764349937438965e-01
5.226933956146240234e-01 9.544755220413208008e-01 7.539969086647033691e-01
5.285560488700866699e-01 9.512906074523925781e-01 7.259945869445800781e-01
5.315514206886291504e-01 9.564292430877685547e-01 6.891824007034301758e-01
5.269100069999694824e-01 9.467492103576660156e-01 7.527468204498291016e-01
5.214509367942810059e-01 9.482995271682739258e-01 7.740847468376159668e-01
5.324009060859680176e-01 9.465931057929992676e-01 6.968711614608764648e-01
5.352808237075805664e-01 9.493461251258850098e-01 6.598548889160156250e-01
5.359319448471069336e-01 9.241502285003662109e-01 6.846292018890380859e-01
5.381639003753662109e-01 9.202622175216674805e-01 6.565521359443664551e-01

sergeyprokudin

import numpy as np

def load_colmap_sparse_points(points3d_path):
  '''Load COLMAP points3D.txt as numpy array
  '''
  with open(points3d_path, 'r') as f:
    points_txt = f.readlines()[3:]

  n_points = len(points_txt)
  points_np = np.zeros([n_points, 6])

sergeyprokudin

def get_img_uv_coords(img):
  """
  Get u,v coords for image pixels
  """
  
  img_height, img_width = img.shape[0], img.shape[1]
  
  uv_map = np.zeros([img_height, img_width, 2])
  u = np.arange(0, img_height)
  v = np.arange(0, img_width)