app

app.py

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+import streamlit as st
+import util
+import torch
+import render_util
+import math
+from pathlib import Path
+from models import PosADANet
+import json
+import plotly.graph_objects as go
+import gdown
+
+
+point_color = "rgb(30, 20, 160)"
+FILE_PC_KEY = 'File'
+DEFAULT_COLOR = '#E1E1E1'
+
+
+@st.cache
+def load_model(path: str, num_controls: int, url: str):
+    """
+    Load model from memory, or download from drive
+    :param path: path to save/load the pretrained model
+    :param num_controls: length of style/control vector the model requires (6 for regular, 8 for metallic roughness)
+    :param url: google drive url to download the model if its not already downloaded
+    :return: returns the pretrained model
+    """
+    if not Path(path).exists():
+        with st.spinner('Downloading Model'):
+            gdown.download(url, path, quiet=False)
+
+    model = PosADANet(1, 4, num_controls, padding='zeros', bilinear=True).to(device)
+    model.load_state_dict(torch.load(path, map_location=device))
+    model.eval()
+
+    return model
+
+
+def load_dict_data(path: str):
+    """
+    load a json file
+    :param path: path to json file
+    :return: dict with json data
+    """
+    with open(path, 'r') as file:
+        data = json.load(file)
+
+    return data
+
+
+def to_rgb(hex_color: str):
+    """
+    convert color in hex format to rgb format
+    :param hex_color: color hex string
+    :return: list of three numbers for RGB channels between 0-1
+    """
+    h = hex_color.lstrip('#')
+    return [float(int(h[i:i + 2], 16)) / 255 for i in (0, 2, 4)]
+
+
+st.title('Z2P - Demo')
+
+device = torch.device(torch.cuda.current_device() if torch.cuda.is_available() else torch.device('cpu'))
+
+st.subheader('Settings')
+
+# Load model and pc data for info about predefined demo point clouds and pretrained models
+model_data = load_dict_data('models/default_settings.json')
+pc_data = load_dict_data('point_clouds/default_settings.json')
+
+col1_head, col2_head = st.columns(2)
+model_key = col2_head.radio(
+         'Choose Model',
+         model_data.keys())
+
+pc_key = col2_head.radio(
+         'Choose Point Cloud',
+         pc_data.keys())
+
+uploaded_file = col2_head.file_uploader('Upload Your Own Point Cloud (.xyz, .obj)')
+
+if pc_key == FILE_PC_KEY:
+    # Use point cloud uploaded by user
+    if uploaded_file is not None:
+        txt = uploaded_file.getvalue().decode("utf-8")
+        pc = util.xyz2tensor(txt, append_normals=True)
+    else:
+        st.warning('Please upload a .xyz or .obj file')
+        st.stop()
+else:
+    # Load demo point cloud
+    pc = util.read_xyz_file(pc_data[pc_key]['path'])
+
+st.header('Input')
+col1, col2 = st.columns(2)
+
+# parameters for point cloud spacial transformations
+col2.subheader("Point Cloud Transformations")
+scale = col2.slider('Scale', min_value=0.0, max_value=5.0, value=pc_data[pc_key]['scale'])
+rx = col2.slider('X-Rotation', min_value=-math.pi, max_value=math.pi, value=pc_data[pc_key]['rx'])
+ry = col2.slider('Y-Rotation', min_value=-math.pi, max_value=math.pi, value=pc_data[pc_key]['ry'])
+rz = col2.slider('Z-Rotation', min_value=-math.pi, max_value=math.pi, value=pc_data[pc_key]['rz'])
+dy = col2.slider('Height', min_value=0, max_value=500, value=pc_data[pc_key]['dy'])
+
+col1.subheader("Input Z-Buffer")
+
+# apply transformations
+pc = render_util.rotate_pc(pc, rx, ry, rz)
+trace1 = [go.Scatter3d(x=pc[:, 0], y=pc[:, 1], z=-pc[:, 2], mode="markers",
+    marker=dict(
+        symbol="circle",
+        size=1,
+        color=point_color))]
+fig = go.Figure(trace1, layout=go.Layout())
+col1_head.plotly_chart(fig, use_container_width=True)
+
+# Project and render the point z-buffer
+zbuffer = render_util.draw_pc(pc, radius=model_data[model_key]['point_radius'], dy=dy, scale=scale)
+
+# Show input z-buffer visualization in streamlit
+col1.image(zbuffer / zbuffer.max(), use_column_width=True)
+
+zbuffer: torch.Tensor = torch.from_numpy(zbuffer).float().to(device)
+
+st.header('Result')
+
+len_style = model_data[model_key]['len_style']
+# Load pretrained model
+model = load_model(model_data[model_key]['path'], len_style, model_data[model_key]['url'])
+col1, col2 = st.columns(2)
+col2.subheader('Visualization Controls')
+zbuffer = zbuffer.unsqueeze(-1).permute(2, 0, 1)
+zbuffer: torch.Tensor = zbuffer.float().to(device).unsqueeze(0)
+
+style = torch.zeros(len_style, dtype=zbuffer.dtype, device=device)
+
+# Pick color and light direction visualization parameters
+hex_color = col2.color_picker('Pick A Color', DEFAULT_COLOR)
+style[0], style[1], style[2] = to_rgb(hex_color)
+style[:3] = style[:3].clip(0.0, 0.9)
+
+# Light direction
+style[3] = col2.slider('Light Radius', min_value=-1.0, max_value=1.0, value=0.0)  # delta_r
+style[4] = col2.slider('Light Phi', min_value=-math.pi/4, max_value=math.pi/4, value=0.0)  # np.pi / 4  # delta_phi
+style[5] = col2.slider('Light Theta', min_value=-math.pi/4, max_value=math.pi/4, value=0.0)  # delta_theta
+
+# Extra Controls for Metallic and Roughness Model
+if len_style == 8:
+    style[6] = col2.slider('Mettalic', min_value=0.0, max_value=1.0, value=0.5)
+    style[7] = col2.slider('Roughness', min_value=0.0, max_value=1.0, value=0.5)
+
+style = style.unsqueeze(0)
+
+# generate image with pretrained model
+with torch.no_grad():
+    generated = model(zbuffer.float(), style)
+
+# embed a white background behind the object using the alpha map
+# as well as the color used as input in the bottom right corner
+generated = util.embed_color(generated.detach(), style[:, :3], box_size=50)
+rendered = generated[0].permute(1, 2, 0).cpu().numpy()
+
+# show the image in streamlit
+col1.image(rendered.clip(0, 1), use_column_width=True)

models.py

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+from networks import *
+
+
+class PosADANet(nn.Module):
+    def encode(self, shp):
+        device = self.omega.device
+        B, _, H, W = shp
+        row = torch.arange(H).to(device) / H
+        enc_row1 = torch.sin(self.omega[None, :] * row[:, None])
+        enc_row2 = torch.cos(self.omega[None, :] * row[:, None])
+        rows = torch.cat([enc_row1.unsqueeze(1).repeat((1, W, 1)), enc_row2.unsqueeze(1).repeat((1, W, 1))], dim=-1)
+
+        col = torch.arange(W).to(device) / W
+        enc_col1 = torch.sin(self.omega[None, :] * col[:, None])
+        enc_col2 = torch.cos(self.omega[None, :] * col[:, None])
+        cols = torch.cat([enc_col1.unsqueeze(0).repeat((H, 1, 1)), enc_col2.unsqueeze(0).repeat((H, 1, 1))], dim=-1)
+
+        encoding = torch.cat([rows, cols], dim=-1)
+        encoding = encoding.permute(2, 0, 1).unsqueeze(0).repeat((B, 1, 1, 1))
+        return encoding
+
+    def get_encoding(self, x):
+        shp1 = x.shape
+        singelton = self.positional_encoding is not None\
+                    and self.positional_encoding.shape[0] == shp1[0] and self.positional_encoding.shape[2:] == shp1[2:]
+        if singelton:
+            return self.positional_encoding
+        self.positional_encoding = self.encode(x.shape)
+        return self.positional_encoding
+
+    def __init__(self, input_channels, output_channels, n_style, bilinear=True, padding='zero', full_ada=True,
+                 nfreq=20, magnitude=10):
+        super(PosADANet, self).__init__()
+        factor = 2 if bilinear else 1
+        self.omega = nn.Parameter(torch.rand(nfreq) * magnitude)
+        self.omega.requires_grad = False
+        self.positional_encoding = None
+        self.full_ada = full_ada
+
+        self.style_encoder = FullyConnected(n_style, W_SIZE, layers=6)
+        self.padding = padding
+        self.input_channels = input_channels + nfreq * 4
+        self.n_classes = output_channels
+        self.bilinear = bilinear
+        self.channels = [512 // factor, 256 // factor, 128 // factor]
+        self.inc = DoubleConv(self.input_channels, 64)
+        self.down1 = Down(64, 128, padding=padding, ada=self.full_ada)
+        self.down2 = Down(128, 256, padding=padding, ada=self.full_ada)
+        self.down3 = Down(256, 512, padding=padding, ada=self.full_ada)
+        self.down4 = Down(512, 1024 // factor, padding=padding, ada=self.full_ada)
+        self.up1 = Up(1024, 512 // factor, bilinear, ada=True, padding=padding)
+        self.up2 = Up(512, 256 // factor, bilinear, ada=True, padding=padding)
+        self.up3 = Up(256, 128 // factor, bilinear, ada=True, padding=padding)
+        self.up4 = Up(128, 64, bilinear, padding=padding, ada=True)
+        self.outc = OutConv(64, output_channels, padding=padding)
+
+    def forward(self, x, style):
+        w = self.style_encoder(style)
+        encoding = self.get_encoding(x)
+        x = torch.cat([x, encoding], dim=1)
+
+        x1 = self.inc(x)
+        if self.full_ada:
+            x2 = self.down1(x1, w=w)
+            x3 = self.down2(x2, w=w)
+            x4 = self.down3(x3, w=w)
+            x5 = self.down4(x4, w=w)
+        else:
+            x2 = self.down1(x1)
+            x3 = self.down2(x2)
+            x4 = self.down3(x3)
+            x5 = self.down4(x4)
+        x = self.up1(x5, x4, w=w)
+        x = self.up2(x, x3, w=w)
+        x = self.up3(x, x2, w=w)
+        x = self.up4(x, x1, w=w)
+        logits = self.outc(x)
+        return logits

models/default_settings.json

@@ -0,0 +1,17 @@
+{
+  "Regular": {
+    "path": "models/model.pt",
+    "point_radius": 3,
+    "len_style": 6,
+    "file_id": "1NqMotLa3kxtmYni8P4kYc4T9rFNsrQlv",
+    "url": "https://drive.google.com/u/0/uc?id=1NqMotLa3kxtmYni8P4kYc4T9rFNsrQlv&export=download"
+  },
+
+  "Metal-Roughness": {
+    "path": "models/mr.pt",
+    "point_radius": 3,
+    "len_style": 8,
+    "file_id": "1A70qTfZSshKewF2udl_yxwwH9Y29Wb_f",
+    "url": "https://drive.google.com/u/0/uc?id=1A70qTfZSshKewF2udl_yxwwH9Y29Wb_f&export=download"
+  }
+}

networks.py

@@ -0,0 +1,169 @@
+import torch
+import torch.nn.functional as F
+from torch import nn
+W_SIZE = 512
+
+
+def calc_mean_std(feat, eps=1e-5):
+    # eps is a small value added to the variance to avoid divide-by-zero.
+    size = feat.size()
+    assert (len(size) == 4)
+    N, C = size[:2]
+    feat_var = feat.view(N, C, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(N, C, 1, 1)
+    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
+    return feat_mean, feat_std
+
+
+def adain(content_feat, style_feat):
+    assert (content_feat.size()[:2] == style_feat[0].size()[:2]) and (content_feat.size()[:2] == style_feat[1].size()[:2])
+    size = content_feat.size()
+    style_mean, style_std = style_feat
+    style_mean, style_std = style_mean.unsqueeze(-1).unsqueeze(-1), style_std.unsqueeze(-1).unsqueeze(-1)
+    content_mean, content_std = calc_mean_std(content_feat)
+
+    normalized_feat = (content_feat - content_mean.expand(
+        size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+class FullyConnected(nn.Module):
+    def __init__(self, input_channels: int, output_channels: int, layers=3):
+        super(FullyConnected, self).__init__()
+        self.channels = torch.linspace(input_channels, output_channels, layers + 1).long()
+        self.layers = nn.Sequential(
+            *[nn.Linear(self.channels[i].item(), self.channels[i+1].item()) for i in range(len(self.channels) - 1)]
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class Affine(nn.Module):
+    def __init__(self, input_channels: int, output_channels):
+        super(Affine, self).__init__()
+        self.lin = nn.Linear(input_channels, output_channels)
+        bias = torch.zeros(output_channels)
+        nn.init.normal_(bias, 0, 1)
+        self.bias = nn.Parameter(bias)
+
+
+    def forward(self, x):
+        return self.lin(x) + self.bias
+
+
+class DoubleConv(nn.Module):
+    """(convolution => [BN] => ReLU) * 2"""
+
+    def __init__(self, in_channels, out_channels, mid_channels=None, ada=False, padding='zeros'):
+        super().__init__()
+        if not mid_channels:
+            mid_channels = out_channels
+        self.ada = ada
+        self.relu = nn.ReLU(inplace=True)
+        self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, padding_mode=padding)
+        if ada:
+            self.a1_mean = Affine(W_SIZE, mid_channels)
+            self.a1_std = Affine(W_SIZE, mid_channels)
+        else:
+            self.norm1 = nn.InstanceNorm2d(mid_channels, affine=True)
+        self.conv2 = nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, padding_mode=padding)
+
+        if ada:
+            self.a2_mean = Affine(W_SIZE, out_channels)
+            self.a2_std = Affine(W_SIZE, out_channels)
+        else:
+            self.norm2 = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x, w=None):
+        if self.ada:
+            assert w is not None
+
+        x = self.conv1(x)
+
+        if self.ada:
+            x = adain(x, (self.a1_mean(w), self.a1_std(w)))
+        else:
+            x = self.norm1(x)
+        x = self.relu(x)
+
+        x = self.conv2(x)
+        if self.ada:
+            x = adain(x, (self.a2_mean(w), self.a2_std(w)))
+        else:
+            x = self.norm2(x)
+        x = self.relu(x)
+
+        return x
+
+
+class DiluteConv(nn.Module):
+    """(convolution => [BN] => ReLU) * 2"""
+
+    def __init__(self, in_channels, out_channels, dilation, padding='zeros'):
+        super().__init__()
+        self.relu = nn.ReLU(inplace=True)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
+                               padding=1+dilation, dilation=dilation, padding_mode=padding)
+        self.norm1 = nn.InstanceNorm2d(out_channels, affine=True)
+
+    def forward(self, x, y=None):
+        if y is not None:
+            x = torch.cat([x, y], dim=1)
+
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu(x)
+        return x
+
+
+class Down(nn.Module):
+    """Downscaling with maxpool then double conv"""
+
+    def __init__(self, in_channels, out_channels, ada=False, padding='zeros'):
+        super().__init__()
+        self.max_pool = nn.MaxPool2d(2)
+        self.double_conv = DoubleConv(in_channels, out_channels, ada=ada, padding=padding)
+
+    def forward(self, x, w=None):
+        x = self.max_pool(x)
+        return self.double_conv(x, w)
+
+
+class Up(nn.Module):
+    """Upscaling then double conv"""
+
+    def __init__(self, in_channels, out_channels, bilinear=True, ada=False, padding='zeros'):
+        super().__init__()
+
+        # if bilinear, use the normal convolutions to reduce the number of channels
+        if bilinear:
+            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2, ada=ada)
+        else:
+            self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
+            self.conv = DoubleConv(in_channels, out_channels, ada=ada)
+
+
+    def forward(self, x1, x2, w=None):
+        x1 = self.up(x1)
+        # input is CHW
+        diffY = x2.size()[2] - x1.size()[2]
+        diffX = x2.size()[3] - x1.size()[3]
+
+        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
+                        diffY // 2, diffY - diffY // 2])
+        # if you have padding issues, see
+        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
+        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
+        x = torch.cat([x2, x1], dim=1)
+        return self.conv(x, w)
+
+
+class OutConv(nn.Module):
+    def __init__(self, in_channels, out_channels, padding='zeros'):
+        super(OutConv, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding_mode=padding)
+
+    def forward(self, x):
+        return self.conv(x)

point_clouds/chair.obj

@@ -0,0 +1,2048 @@
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point_clouds/default_settings.json

@@ -0,0 +1,28 @@
+{
+  "Goat": {
+    "rx": -1.15,
+    "ry": 0.64,
+    "rz": -2.46,
+    "scale": 1.25,
+    "path": "point_clouds/goat.obj",
+    "dy": 331
+  },
+
+  "Chair": {
+  "rx": -2.08,
+  "ry": 0.13,
+  "rz": 1.46,
+  "scale": 1.09,
+  "path": "point_clouds/chair.obj",
+  "dy": 324
+  },
+
+  "File": {
+    "rx": 0.0,
+    "ry": 0.0,
+    "rz": 0.0,
+    "scale": 1.0,
+    "path": "None",
+    "dy": 300
+    }
+}

render_util.py

@@ -0,0 +1,135 @@
+import torch
+import numpy as np
+import math
+import cv2
+
+world_mat_object = torch.tensor([
+    [0.5085, 0.3226, 0.7984, 0.0000],
+    [-0.3479, 0.9251, -0.1522, 0.0000],
+    [-0.7877, -0.2003, 0.5826, 0.3384],
+    [0.0000, 0.0000, 0.0000, 1.0000]
+])
+
+world_mat_inv = torch.tensor([
+    [0.4019, 0.9157, 0.0000, 0.3359],
+    [-0.1932, 0.0848, 0.9775, -1.0227],
+    [0.8951, -0.3928, 0.2110, -7.0748],
+    [-0.0000, 0.0000, -0.0000, 1.0000]
+])
+
+
+proj = torch.tensor([
+    [2.1875, 0.0000, 0.0000, 0.0000],
+    [0.0000, 3.8889, 0.0000, 0.0000],
+    [0.0000, 0.0000, -1.0020, -0.2002],
+    [0.0000, 0.0000, -1.0000, 0.0000]
+])
+
+
+RANGES = [[0, 540], [100, 960]]
+
+TARGET = [500, -1]
+
+
+def resize(img):
+    if TARGET[1] == -1:
+        r = img.shape[0] / img.shape[1]
+        img = cv2.resize(img, (TARGET[0], int(r * TARGET[0])))
+    else:
+        img = cv2.resize(img, (TARGET[0], TARGET[1]))
+
+    return img
+
+
+def scatter(u_pix, v_pix, distances, res, radius=5):
+    distances -= 6
+    img = np.zeros(res)
+    for (u, v, d) in zip(u_pix, v_pix, distances):
+        v, u = int(v), int(u)
+        f = np.exp(-d / 0.7)
+        if radius == 0:
+            img[v, u] = max(img[v, u], f)
+        else:
+            for t1 in range(-radius, radius):
+                for t2 in range(-radius, radius):
+                    ty, tx = v - t1, u - t2
+                    ty, tx = max(0, ty), max(0, tx)
+                    ty, tx = min(res[0] - 1, ty), min(res[1] - 1, tx)
+                    img[ty, tx] = max(img[ty, tx], f)
+
+    return img
+
+
+def generate_roation(phi_x, phi_y, phi_z):
+    def Rx(theta):
+        return torch.tensor([[1, 0, 0],
+                          [0, math.cos(theta), -math.sin(theta)],
+                          [0, math.sin(theta), math.cos(theta)]])
+
+    def Ry(theta):
+        return torch.tensor([[math.cos(theta), 0, math.sin(theta)],
+                          [0, 1, 0],
+                          [-math.sin(theta), 0, math.cos(theta)]])
+
+    def Rz(theta):
+        return torch.tensor([[math.cos(theta), -math.sin(theta), 0],
+                          [math.sin(theta), math.cos(theta), 0],
+                          [0, 0, 1]])
+
+    return Rz(phi_z) @ Ry(phi_y) @ Rx(phi_x)
+
+
+def rotate_pc(pc, rx, ry, rz):
+    rotation = generate_roation(rx, ry, rz)
+    rotated = pc.clone()
+    rotated[:, :3] = rotated[:, :3] @ rotation.T
+    if rotated.shape[-1] == 6:
+        rotated[:, 3:] = rotated[:, 3:] @ rotation.T
+    return rotated
+
+
+def draw_pc(pc: torch.Tensor, res=(540, 960), radius=5, timer=None, dy=0, scale=1):
+    xyz = pc[:, :3]
+    xyz -= xyz.mean(dim=0)
+    t_scale = xyz.norm(dim=-1).max()
+    xyz /= t_scale
+    xyz *= scale
+
+    xyz[:, -1] += xyz[:, -1].min()
+
+    n, _ = xyz.shape
+
+    if timer is not None:
+        with timer('project'):
+            xyz_pad = torch.cat([xyz, torch.ones_like(pc[:, :1])], dim=-1)
+            xyz_local = xyz_pad @ world_mat_inv.T
+            distances = -xyz_local[:, 2]
+
+            projected = xyz_local @ proj.T
+            projected = projected / projected[:, 3:4]
+            projected = projected[:, :3]
+
+            u_pix = ((projected[0] + 1) / 2) * res[1]
+            v_pix = ((projected[1] + 1) / 2) * res[0] + dy
+
+        with timer('z-buffer'):
+            z_buffer = scatter(u_pix, v_pix, distances, res, radius=radius)[:, :]
+    else:
+        xyz_pad = torch.cat([xyz, torch.ones_like(pc[:, :1])], dim=-1)
+        xyz_local = xyz_pad @ world_mat_inv.T
+        distances = -xyz_local[:, 2]
+
+        projected = xyz_local @ proj.T
+        projected = projected / projected[:, 3:4]
+        projected = projected[:, :3]
+
+        u_pix = ((projected[:, 0] + 1) / 2) * res[1]
+        v_pix = ((projected[:, 1] + 1) / 2) * res[0] + dy
+
+        z_buffer = scatter(u_pix, v_pix, distances, res, radius=radius)[:, :]
+
+    z_buffer = z_buffer[RANGES[0][0]: RANGES[0][1], :]
+    z_buffer = z_buffer[:, RANGES[1][0]:RANGES[1][1]]
+    z_buffer = resize(z_buffer)
+    return z_buffer
+

requirements.txt

@@ -0,0 +1,9 @@
+matplotlib==3.5.2
+numpy==1.22.3
+opencv-python==4.5.5.64
+Tqdm
+torch==1.8.1
+plotly
+gdown
+
+

util.py

@@ -0,0 +1,121 @@
+import torch
+import math
+from pathlib import Path
+import time
+
+
+def euler_rotation(rx=0, ry=0, rz=0, backwards=False):
+    Ms = []
+    if rz != 0:
+        cosz = math.cos(rz)
+        sinz = math.sin(rz)
+        Ms.append(torch.tensor(
+            [[cosz, -sinz, 0],
+             [sinz, cosz, 0],
+             [0, 0, 1]]))
+    if ry != 0:
+        cosy = math.cos(ry)
+        siny = math.sin(ry)
+        Ms.append(torch.tensor(
+            [[cosy, 0, siny],
+             [0, 1, 0],
+             [-siny, 0, cosy]]))
+    if rx != 0:
+        cosx = math.cos(rx)
+        sinx = math.sin(rx)
+        Ms.append(torch.tensor(
+            [[1, 0, 0],
+             [0, cosx, -sinx],
+             [0, sinx, cosx]]))
+
+    rotation = torch.eye(3)
+    if backwards and len(Ms) > 0:
+        Ms = Ms[::-1]
+
+    for mat in Ms[::-1]:
+        rotation = torch.matmul(rotation, mat)
+    return rotation
+
+
+def export(file, vs, faces, vn=None, color=None):
+    with open(file, 'w+') as f:
+        for vi, v in enumerate(vs):
+            if color is None:
+                f.write("v %f %f %f\n" % (v[0], v[1], v[2]))
+            else:
+                f.write("v %f %f %f %f %f %f\n" % (v[0], v[1], v[2], color[vi][0], color[vi][1], color[vi][2]))
+            if vn is not None:
+                f.write("vn %f %f %f\n" % (vn[vi, 0], vn[vi, 1], vn[vi, 2]))
+        for face in faces:
+            f.write("f %d %d %d\n" % (face[0] + 1, face[1] + 1, face[2] + 1))
+
+
+def xyz2tensor(txt, append_normals=False):
+    pts = []
+    for line in txt.split('\n'):
+        line = line.strip()
+        line = line.lstrip('v ')
+        spt = line.split(' ')
+        if 'nan' in line:
+            continue
+        if len(spt) == 6:
+            pts.append(torch.tensor([float(x) for x in spt]))
+        if len(spt) == 3:
+            t = [float(x) for x in spt]
+            if append_normals:
+                t += [0.0 for _ in range(3)]
+            pts.append(torch.tensor(t))
+
+    rtn = torch.stack(pts, dim=0)
+    return rtn
+
+
+def read_xyz_file(path: Path):
+    with open(path, 'r') as file:
+        return xyz2tensor(file.read(), append_normals=True)
+
+
+def embed_color(img: torch.Tensor, color, box_size=70):
+    shp = img.shape
+    D2 = [shp[2] - box_size, shp[2]]
+    D3 = [shp[3] - box_size, shp[3]]
+    img = img.clone()
+    img[:, :3, D2[0]:D2[1], D3[0]:D3[1]] = color[:, :, None, None]
+    if img.shape[1] == 4:
+        img[:, -1, D2[0]:D2[1], D3[0]:D3[1]] = 1
+    return img
+
+
+def get_n_params(model):
+    pp=0
+    for p in list(model.parameters()):
+        nn=1
+        for s in list(p.size()):
+            nn = nn*s
+        pp += nn
+    return pp
+
+
+def xyz2tensor(txt, append_normals=False):
+    pts = []
+    for line in txt.split('\n'):
+        line = line.strip()
+        line = line.lstrip('v ')
+        spt = line.split(' ')
+        if 'nan' in line:
+            continue
+        if len(spt) == 6:
+            pts.append(torch.tensor([float(x) for x in spt]))
+        if len(spt) == 3:
+            t = [float(x) for x in spt]
+            if append_normals:
+                t += [0.0 for _ in range(3)]
+            pts.append(torch.tensor(t))
+
+    rtn = torch.stack(pts, dim=0)
+    return rtn
+
+
+def read_xyz_file(path: Path):
+    with open(path, 'r') as file:
+        return xyz2tensor(file.read(), append_normals=True)