sergeyprokudin’s gists

sergeyprokudin / siren.py

Created June 25, 2024 10:31

Siren model

	# @title Define SIREN deformation model

	# https://github.com/vsitzmann/siren

	# MIT License

	# Copyright (c) 2020 Vincent Sitzmann

	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal

sergeyprokudin / positional_embedding.py

Created May 18, 2024 09:55

Simple NeRF positional embedding

	# simple positional embedding (NeRF style)
	# as in https://arxiv.org/pdf/2003.08934 (Equation 4)

	def positional_embedding(x, k):

	batch_size, n_dims = x.shape
	x_rep = x.unsqueeze(-1).expand(-1, -1, k)

	k_pows = torch.pow(2, torch.arange(0, k, device=x.device)).view(1, 1, k)
	x_k = k_pows * torch.pi * x_rep

sergeyprokudin / laplacian_smoothing.py

Last active April 15, 2024 11:23

	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 / normals_poisson_meshlab.py

Created February 24, 2024 10:39

Estimate normals and reconstruct mesh via Poisson

	# 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 / sharp_image_filter.py

Created February 21, 2024 17:33

Filter out blurry images based on the image Laplacian

	"""
	MIT License

	Copyright (c) 2024 Sergey Prokudin [email protected]

	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 / point_renderer_depth.py

Last active October 19, 2023 21:13

	# 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 / poisson_open3d.py

Created March 6, 2023 10:14

	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 / llff_poses_to_pytorch3d_camera.ipynb

Last active May 24, 2023 00:00

llff_poses_to_pytorch3d_camera.ipynb

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sergeyprokudin / rp_mei_posed_001.mtl

Last active August 13, 2021 12:29

Material file for RenderPeople free "Mei" model (https://renderpeople.com/sample/rp_free_posed_people_OBJ.zip)

	newmtl material_1
	map_Kd rp_mei_posed_001_dif.jpg

sergeyprokudin / smpl_verts_colors.txt

Created July 15, 2021 22:17

Sergey Prokudin sergeyprokudin