SYMBOL INDEX (330 symbols across 48 files)

FILE: dataloader/base.py
  class Dataset (line 14) | class Dataset(torch.utils.data.Dataset):
    method __init__ (line 15) | def __init__(
    method __len__ (line 36) | def __len__(self):
    method __getitem__ (line 39) | def __getitem__(self, idx):
    method sample_pointcloud (line 42) | def sample_pointcloud(self, csvfile, pc_size):
    method labeled_sampling (line 54) | def labeled_sampling(self, f, subsample, pc_size=1024, load_from_path=...
    method get_instance_filenames (line 92) | def get_instance_filenames(self, data_source, split, gt_filename="sdf_...

FILE: dataloader/modulation_loader.py
  class ModulationLoader (line 17) | class ModulationLoader(torch.utils.data.Dataset):
    method __init__ (line 18) | def __init__(self, data_path, pc_path=None, split_file=None, pc_size=N...
    method __len__ (line 45) | def __len__(self):
    method __getitem__ (line 48) | def __getitem__(self, index):
    method load_modulations (line 57) | def load_modulations(self, data_source, pc_source, split, f_name="late...
    method unconditional_load_modulations (line 85) | def unconditional_load_modulations(self, data_source, split, f_name="l...

FILE: dataloader/pc_loader.py
  class PCloader (line 15) | class PCloader(base.Dataset):
    method __init__ (line 17) | def __init__(
    method get_all_files (line 41) | def get_all_files(self):
    method __getitem__ (line 44) | def __getitem__(self, idx):
    method __len__ (line 51) | def __len__(self):
    method sample_pc (line 55) | def sample_pc(self, f, samp=1024):

FILE: dataloader/sdf_loader.py
  class SdfLoader (line 17) | class SdfLoader(base.Dataset):
    method __init__ (line 19) | def __init__(
    method __getitem__ (line 62) | def __getitem__(self, idx):
    method __len__ (line 87) | def __len__(self):

FILE: diff_utils/helpers.py
  function get_split_filenames (line 13) | def get_split_filenames(data_source, split_file, f_name="sdf_data.csv"):
  function sample_pc (line 26) | def sample_pc(f, samp=1024, add_flip_augment=False):
  function perturb_point_cloud (line 45) | def perturb_point_cloud(pc, perturb, pc_size=None, crop_percent=0.25):
  function fps (line 60) | def fps(data, number):
  function crop_pc (line 69) | def crop_pc(xyz, crop, pc_size=None, fixed_points = None, padding_zeros ...
  function visualize_pc (line 138) | def visualize_pc(pc):
  function jitter_pc (line 144) | def jitter_pc(pc, pc_size=None, sigma=0.05, clip=0.1):
  function normalize_pc (line 156) | def normalize_pc(pc):
  function save_model (line 163) | def save_model(iters, model, optimizer, loss, path):
  function load_model (line 170) | def load_model(model, optimizer, path):
  function save_code_to_conf (line 185) | def save_code_to_conf(conf_dir):
  class ScheduledOpt (line 195) | class ScheduledOpt:
    method __init__ (line 200) | def __init__(self, warmup, optimizer):
    method step (line 221) | def step(self):
    method zero_grad (line 231) | def zero_grad(self):
    method rate (line 234) | def rate(self, step = None):
  function exists (line 245) | def exists(x):
  function default (line 248) | def default(val, d):
  function cycle (line 253) | def cycle(dl):
  function has_int_squareroot (line 258) | def has_int_squareroot(num):
  function num_to_groups (line 261) | def num_to_groups(num, divisor):
  function convert_image_to (line 269) | def convert_image_to(img_type, image):
  function normalize_to_neg_one_to_one (line 277) | def normalize_to_neg_one_to_one(img):
  function unnormalize_to_zero_to_one (line 281) | def unnormalize_to_zero_to_one(t):
  function normalize_to_zero_to_one (line 286) | def normalize_to_zero_to_one(f):
  function extract (line 293) | def extract(a, t, x_shape):
  function linear_beta_schedule (line 298) | def linear_beta_schedule(timesteps):
  function cosine_beta_schedule (line 305) | def cosine_beta_schedule(timesteps, s = 0.008):

FILE: diff_utils/model_utils.py
  class LayerNorm (line 16) | class LayerNorm(nn.Module):
    method __init__ (line 17) | def __init__(self, dim, eps = 1e-5, stable = False):
    method forward (line 23) | def forward(self, x):
  class MLP (line 33) | class MLP(nn.Module):
    method __init__ (line 34) | def __init__(
    method forward (line 63) | def forward(self, x):
  class RelPosBias (line 68) | class RelPosBias(nn.Module):
    method __init__ (line 69) | def __init__(
    method _relative_position_bucket (line 81) | def _relative_position_bucket(
    method forward (line 96) | def forward(self, i, j, *, device):
  class SwiGLU (line 106) | class SwiGLU(nn.Module):
    method forward (line 108) | def forward(self, x):
  function FeedForward (line 112) | def FeedForward(
  class SinusoidalPosEmb (line 134) | class SinusoidalPosEmb(nn.Module):
    method __init__ (line 135) | def __init__(self, dim):
    method forward (line 139) | def forward(self, x):
  function exists (line 148) | def exists(x):
  function default (line 151) | def default(val, d):
  class Attention (line 156) | class Attention(nn.Module):
    method __init__ (line 157) | def __init__(
    method forward (line 197) | def forward(self, x, context=None, mask = None, attn_bias = None):

FILE: diff_utils/pointnet/conv_pointnet.py
  class ConvPointnet (line 9) | class ConvPointnet(nn.Module):
    method __init__ (line 26) | def __init__(self, c_dim=512, dim=3, hidden_dim=128, scatter_type='max',
    method forward (line 58) | def forward(self, p, query):
    method normalize_coordinate (line 100) | def normalize_coordinate(self, p, padding=0.1, plane='xz'):
    method coordinate2index (line 126) | def coordinate2index(self, x, reso):
    method pool_local (line 143) | def pool_local(self, xy, index, c):
    method generate_plane_features (line 159) | def generate_plane_features(self, p, c, plane='xz'):
    method sample_plane_feature (line 179) | def sample_plane_feature(self, query, plane_feature, plane):
  function conv3x3 (line 187) | def conv3x3(in_channels, out_channels, stride=1,
  function upconv2x2 (line 198) | def upconv2x2(in_channels, out_channels, mode='transpose'):
  function conv1x1 (line 212) | def conv1x1(in_channels, out_channels, groups=1):
  class DownConv (line 221) | class DownConv(nn.Module):
    method __init__ (line 226) | def __init__(self, in_channels, out_channels, pooling=True):
    method forward (line 239) | def forward(self, x):
  class UpConv (line 248) | class UpConv(nn.Module):
    method __init__ (line 253) | def __init__(self, in_channels, out_channels,
    method forward (line 274) | def forward(self, from_down, from_up):
  class UNet (line 290) | class UNet(nn.Module):
    method __init__ (line 313) | def __init__(self, num_classes, in_channels=3, depth=5,
    method weight_init (line 387) | def weight_init(m):
    method reset_params (line 393) | def reset_params(self):
    method forward (line 398) | def forward(self, x):
  class ResnetBlockFC (line 415) | class ResnetBlockFC(nn.Module):
    method __init__ (line 423) | def __init__(self, size_in, size_out=None, size_h=None):
    method forward (line 447) | def forward(self, x):

FILE: diff_utils/pointnet/dgcnn.py
  function knn (line 5) | def knn(x, k):
  function get_graph_feature (line 13) | def get_graph_feature(x, k=20):
  class DGCNN (line 35) | class DGCNN(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 76) | def forward(self, x):
    method get_global_feature (line 111) | def get_global_feature(self, x):

FILE: diff_utils/pointnet/pointnet_base.py
  class PointNetBase (line 14) | class PointNetBase(nn.Module):
    method __init__ (line 16) | def __init__(self, num_points=2000, K=3):
    method forward (line 55) | def forward(self, x):

FILE: diff_utils/pointnet/pointnet_classifier.py
  class PointNetClassifier (line 14) | class PointNetClassifier(nn.Module):
    method __init__ (line 16) | def __init__(self, num_points=2000, K=3):
    method forward (line 38) | def forward(self, x):

FILE: diff_utils/pointnet/transformer.py
  class Transformer (line 14) | class Transformer(nn.Module):
    method __init__ (line 16) | def __init__(self, num_points=2000, K=3):
    method forward (line 62) | def forward(self, x):

FILE: diff_utils/sdf_utils.py
  function pred_sdf_loss (line 26) | def pred_sdf_loss(x0, idx):
  function functional_sdf_model (line 36) | def functional_sdf_model(modulation, xyz, gt):
  function sdf_sampling (line 51) | def sdf_sampling(f, subsample, pc_size=1024, batch=1):
  function apply_to_sdf (line 91) | def apply_to_sdf(f, x):

FILE: metrics/StructuralLosses/match_cost.py
  class MatchCostFunction (line 6) | class MatchCostFunction(Function):
    method forward (line 10) | def forward(ctx, seta, setb):
    method backward (line 31) | def backward(ctx, grad_output):

FILE: metrics/StructuralLosses/nn_distance.py
  class NNDistanceFunction (line 7) | class NNDistanceFunction(Function):
    method forward (line 11) | def forward(ctx, seta, setb):
    method backward (line 28) | def backward(ctx, grad_dist1, grad_dist2):

FILE: metrics/evaluation_metrics.py
  function distChamfer (line 12) | def distChamfer(a, b):
  function distChamferCUDA (line 30) | def distChamferCUDA(x, y):
  function emd_approx (line 38) | def emd_approx(x, y):
  function emd_approx_cuda (line 61) | def emd_approx_cuda(sample, ref):
  function EMD_CD (line 73) | def EMD_CD(sample_pcs, ref_pcs, batch_size, accelerated_cd=False, reduce...
  function _pairwise_EMD_CD_ (line 114) | def _pairwise_EMD_CD_(sample_pcs, ref_pcs, batch_size=None, accelerated_...
  function emd_tmd_from_pcs (line 159) | def emd_tmd_from_pcs(gen_pcs):
  function knn (line 173) | def knn(Mxx, Mxy, Myy, k, sqrt=False):
  function lgan_mmd_cov (line 205) | def lgan_mmd_cov(all_dist):
  function compute_mmd (line 221) | def compute_mmd(sample_pcs, ref_pcs, batch_size=None, accelerated_cd=True):
  function compute_cd (line 241) | def compute_cd(sample_pcs, ref_pcs):
  function compute_all_metrics (line 249) | def compute_all_metrics(sample_pcs, ref_pcs, batch_size=None, accelerate...
  function unit_cube_grid_point_cloud (line 286) | def unit_cube_grid_point_cloud(resolution, clip_sphere=False):
  function jsd_between_point_cloud_sets (line 307) | def jsd_between_point_cloud_sets(sample_pcs, ref_pcs, resolution=28):
  function entropy_of_occupancy_grid (line 321) | def entropy_of_occupancy_grid(pclouds, grid_resolution, in_sphere=False,...
  function jensen_shannon_divergence (line 363) | def jensen_shannon_divergence(P, Q):
  function _jsdiv (line 385) | def _jsdiv(P, Q):

FILE: metrics/pytorch_structural_losses/StructuralLosses/match_cost.py
  class MatchCostFunction (line 6) | class MatchCostFunction(Function):
    method forward (line 10) | def forward(ctx, seta, setb):
    method backward (line 31) | def backward(ctx, grad_output):

FILE: metrics/pytorch_structural_losses/StructuralLosses/nn_distance.py
  class NNDistanceFunction (line 7) | class NNDistanceFunction(Function):
    method forward (line 11) | def forward(ctx, seta, setb):
    method backward (line 28) | def backward(ctx, grad_dist1, grad_dist2):

FILE: metrics/pytorch_structural_losses/match_cost.py
  class MatchCostFunction (line 6) | class MatchCostFunction(Function):
    method forward (line 10) | def forward(ctx, seta, setb):
    method backward (line 31) | def backward(ctx, grad_output):

FILE: metrics/pytorch_structural_losses/nn_distance.py
  class NNDistanceFunction (line 7) | class NNDistanceFunction(Function):
    method forward (line 11) | def forward(ctx, seta, setb):
    method backward (line 28) | def backward(ctx, grad_dist1, grad_dist2):

FILE: metrics/pytorch_structural_losses/pybind/bind.cpp
  function PYBIND11_MODULE (line 9) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m){

FILE: metrics/pytorch_structural_losses/src/structural_loss.cpp
  function ApproxMatch (line 22) | std::vector<at::Tensor> ApproxMatch(at::Tensor set_d, at::Tensor set_q) {
  function MatchCost (line 39) | at::Tensor MatchCost(at::Tensor set_d, at::Tensor set_q, at::Tensor matc...
  function MatchCostGrad (line 54) | std::vector<at::Tensor> MatchCostGrad(at::Tensor set_d, at::Tensor set_q...
  function NNDistance (line 80) | std::vector<at::Tensor> NNDistance(at::Tensor set_d, at::Tensor set_q) {
  function NNDistanceGrad (line 101) | std::vector<at::Tensor> NNDistanceGrad(at::Tensor set_d, at::Tensor set_...

FILE: metrics/pytorch_structural_losses/src/utils.hpp
  class Formatter (line 5) | class Formatter {
    method Formatter (line 7) | Formatter() {}
    method Formatter (line 10) | Formatter &operator<<(const Type &value) {
    method str (line 15) | std::string str() const { return stream_.str(); }
    type ConvertToString (line 18) | enum ConvertToString { to_str }

FILE: models/archs/diffusion_arch.py
  class CausalTransformer (line 16) | class CausalTransformer(nn.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 87) | def forward(self, x, time_emb=None, context=None):
  class DiffusionNet (line 123) | class DiffusionNet(nn.Module):
    method __init__ (line 125) | def __init__(
    method forward (line 160) | def forward(

FILE: models/archs/encoders/auto_decoder.py
  class AutoDecoder (line 9) | class AutoDecoder():
    method __init__ (line 12) | def __init__(self, num_scenes, latent_size):
    method build_model (line 16) | def build_model(self):

FILE: models/archs/encoders/conv_pointnet.py
  class ConvPointnet (line 9) | class ConvPointnet(nn.Module):
    method __init__ (line 26) | def __init__(self, c_dim=512, dim=3, hidden_dim=128, scatter_type='max',
    method generate_plane_features (line 56) | def generate_plane_features(self, p, c, plane='xz'):
    method forward (line 75) | def forward(self, p, query):
    method forward_with_plane_features (line 119) | def forward_with_plane_features(self, plane_features, query):
    method forward_with_pc_features (line 135) | def forward_with_pc_features(self, c, p, query):
    method get_point_cloud_features (line 153) | def get_point_cloud_features(self, p):
    method get_plane_features (line 181) | def get_plane_features(self, p):
    method normalize_coordinate (line 195) | def normalize_coordinate(self, p, padding=0.1, plane='xz'):
    method coordinate2index (line 221) | def coordinate2index(self, x, reso):
    method pool_local (line 238) | def pool_local(self, xy, index, c):
    method sample_plane_feature (line 255) | def sample_plane_feature(self, query, plane_feature, plane):
  function conv3x3 (line 263) | def conv3x3(in_channels, out_channels, stride=1,
  function upconv2x2 (line 274) | def upconv2x2(in_channels, out_channels, mode='transpose'):
  function conv1x1 (line 288) | def conv1x1(in_channels, out_channels, groups=1):
  class DownConv (line 297) | class DownConv(nn.Module):
    method __init__ (line 302) | def __init__(self, in_channels, out_channels, pooling=True):
    method forward (line 315) | def forward(self, x):
  class UpConv (line 324) | class UpConv(nn.Module):
    method __init__ (line 329) | def __init__(self, in_channels, out_channels,
    method forward (line 350) | def forward(self, from_down, from_up):
  class UNet (line 366) | class UNet(nn.Module):
    method __init__ (line 389) | def __init__(self, num_classes, in_channels=3, depth=5,
    method weight_init (line 465) | def weight_init(m):
    method reset_params (line 471) | def reset_params(self):
    method forward (line 476) | def forward(self, x):
    method generate (line 497) | def generate(self, x):
  class ResnetBlockFC (line 501) | class ResnetBlockFC(nn.Module):
    method __init__ (line 509) | def __init__(self, size_in, size_out=None, size_h=None):
    method forward (line 533) | def forward(self, x):

FILE: models/archs/encoders/dgcnn.py
  function knn (line 5) | def knn(x, k):
  function get_graph_feature (line 13) | def get_graph_feature(x, k=20):
  class DGCNN (line 35) | class DGCNN(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 65) | def forward(self, x):

FILE: models/archs/encoders/rbf.py
  class RBFLayer (line 11) | class RBFLayer(nn.Module):
    method __init__ (line 16) | def __init__(self, in_features=3, out_features=1024):
    method reset_parameters (line 26) | def reset_parameters(self):
    method forward (line 30) | def forward(self, x):
    method gaussian (line 38) | def gaussian(self, alpha):

FILE: models/archs/encoders/sal_pointnet.py
  class SalPointNet (line 13) | class SalPointNet(nn.Module):
    method __init__ (line 21) | def __init__(self, c_dim=256, in_dim=3, hidden_dim=128):
    method forward (line 41) | def forward(self, p):
    method maxpool (line 64) | def maxpool(self, x, dim=-1, keepdim=False):

FILE: models/archs/encoders/vanilla_pointnet.py
  class PointNet (line 13) | class PointNet(MetaModule):
    method __init__ (line 14) | def __init__(self, latent_size):
    method forward (line 43) | def forward(self, x, params=None):

FILE: models/archs/modulated_sdf.py
  class Layer (line 14) | class Layer(nn.Module):
    method __init__ (line 15) | def __init__(self, dim_in=512, dim_out=512, dim=512, dropout_prob=0.0,...
    method forward (line 36) | def forward(self, x):
  class ModulatedMLP (line 45) | class ModulatedMLP(nn.Module):
    method __init__ (line 46) | def __init__(self, latent_size=512, hidden_dim=512, num_layers=9, late...
    method pe_transform (line 116) | def pe_transform(self, data):
    method forward (line 119) | def forward(self, xyz, latent):

FILE: models/archs/resnet_block.py
  class ResnetBlockFC (line 6) | class ResnetBlockFC(nn.Module):
    method __init__ (line 14) | def __init__(self, size_in, size_out=None, size_h=None):
    method forward (line 38) | def forward(self, x):

FILE: models/archs/sdf_decoder.py
  class SdfDecoder (line 10) | class SdfDecoder(nn.Module):
    method __init__ (line 11) | def __init__(self, latent_size=256, hidden_dim=512,
    method forward (line 65) | def forward(self, x):

FILE: models/archs/unet.py
  function conv3x3 (line 14) | def conv3x3(in_channels, out_channels, stride=1,
  function upconv2x2 (line 25) | def upconv2x2(in_channels, out_channels, mode='transpose'):
  function conv1x1 (line 39) | def conv1x1(in_channels, out_channels, groups=1):
  class DownConv (line 48) | class DownConv(nn.Module):
    method __init__ (line 53) | def __init__(self, in_channels, out_channels, pooling=True):
    method forward (line 66) | def forward(self, x):
  class UpConv (line 75) | class UpConv(nn.Module):
    method __init__ (line 80) | def __init__(self, in_channels, out_channels,
    method forward (line 101) | def forward(self, from_down, from_up):
  class UNet (line 117) | class UNet(nn.Module):
    method __init__ (line 140) | def __init__(self, num_classes, in_channels=3, depth=5,
    method weight_init (line 214) | def weight_init(m):
    method reset_params (line 220) | def reset_params(self):
    method forward (line 225) | def forward(self, x):

FILE: models/autoencoder.py
  class BetaVAE (line 11) | class BetaVAE(nn.Module):
    method __init__ (line 15) | def __init__(self,
    method encode (line 102) | def encode(self, enc_input: Tensor) -> List[Tensor]:
    method decode (line 120) | def decode(self, z: Tensor) -> Tensor:
    method reparameterize (line 131) | def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:
    method forward (line 143) | def forward(self, data: Tensor, **kwargs) -> Tensor:
    method loss_function (line 149) | def loss_function(self,
    method sample (line 182) | def sample(self,
    method generate (line 199) | def generate(self, x: Tensor, **kwargs) -> Tensor:
    method get_latent (line 208) | def get_latent(self, x):

FILE: models/combined_model.py
  class CombinedModel (line 9) | class CombinedModel(pl.LightningModule):
    method __init__ (line 10) | def __init__(self, specs):
    method training_step (line 29) | def training_step(self, x, idx):
    method configure_optimizers (line 39) | def configure_optimizers(self):
    method train_modulation (line 67) | def train_modulation(self, x):
    method train_diffusion (line 101) | def train_diffusion(self, x):
    method train_combined (line 127) | def train_combined(self, x):

FILE: models/diff_np_if_torch_error.py
  class DiffusionModel (line 27) | class DiffusionModel(nn.Module):
    method __init__ (line 28) | def __init__(
    method predict_start_from_noise (line 123) | def predict_start_from_noise(self, x_t, t, noise):
    method predict_noise_from_start (line 129) | def predict_noise_from_start(self, x_t, t, x0):
    method ddim_sample (line 136) | def ddim_sample(self, dim, batch_size, noise=None, clip_denoised = Tru...
    method sample (line 175) | def sample(self, dim, batch_size, noise=None, clip_denoised = True, tr...
    method q_posterior (line 205) | def q_posterior(self, x_start, x_t, t):
    method q_sample (line 216) | def q_sample(self, x_start, t, noise=None):
    method forward (line 227) | def forward(self, x_start, t, ret_pred_x=False, noise = None, cond=None):
    method model_predictions (line 258) | def model_predictions(self, model_input, t):
    method diffusion_model_from_latent (line 281) | def diffusion_model_from_latent(self, x_start, cond=None):
    method generate_from_pc (line 299) | def generate_from_pc(self, pc, load_pc=False, batch=5, save_pc=False, ...
    method generate_unconditional (line 337) | def generate_unconditional(self, num_samples):

FILE: models/diffusion.py
  class DiffusionModel (line 27) | class DiffusionModel(nn.Module):
    method __init__ (line 28) | def __init__(
    method predict_start_from_noise (line 94) | def predict_start_from_noise(self, x_t, t, noise):
    method predict_noise_from_start (line 100) | def predict_noise_from_start(self, x_t, t, x0):
    method ddim_sample (line 107) | def ddim_sample(self, dim, batch_size, noise=None, clip_denoised = Tru...
    method sample (line 146) | def sample(self, dim, batch_size, noise=None, clip_denoised = True, tr...
    method q_posterior (line 176) | def q_posterior(self, x_start, x_t, t):
    method q_sample (line 187) | def q_sample(self, x_start, t, noise=None):
    method forward (line 198) | def forward(self, x_start, t, ret_pred_x=False, noise = None, cond=None):
    method model_predictions (line 229) | def model_predictions(self, model_input, t):
    method diffusion_model_from_latent (line 252) | def diffusion_model_from_latent(self, x_start, cond=None):
    method generate_from_pc (line 270) | def generate_from_pc(self, pc, load_pc=False, batch=5, save_pc=False, ...
    method generate_unconditional (line 308) | def generate_unconditional(self, num_samples):

FILE: models/sdf_model.py
  class SdfModel (line 21) | class SdfModel(pl.LightningModule):
    method __init__ (line 23) | def __init__(self, specs):
    method configure_optimizers (line 42) | def configure_optimizers(self):
    method training_step (line 48) | def training_step(self, x, idx):
    method forward (line 65) | def forward(self, pc, xyz):
    method forward_with_plane_features (line 70) | def forward_with_plane_features(self, plane_features, xyz):

FILE: test.py
  function test_modulations (line 30) | def test_modulations():
  function test_generation (line 94) | def test_generation():

FILE: train.py
  function train (line 32) | def train():

FILE: utils/chamfer.py
  function compute_trimesh_chamfer (line 5) | def compute_trimesh_chamfer(gt_points, gen_points, offset=0, scale=1):
  function scale_to_unit_sphere (line 31) | def scale_to_unit_sphere(points):

FILE: utils/evaluate.py
  function main (line 16) | def main(gt_pc, recon_mesh, out_file, mesh_name, return_value=False, ret...
  function calc_cd (line 53) | def calc_cd(gt_pc, recon_pc):
  function single_eval (line 69) | def single_eval(gt_csv, recon_mesh):

FILE: utils/mesh.py
  function create_mesh (line 15) | def create_mesh(
  function create_cube (line 62) | def create_cube(N):
  function convert_sdf_samples_to_ply (line 89) | def convert_sdf_samples_to_ply(

FILE: utils/reconstruct.py
  function vis_recon (line 23) | def vis_recon(test_dataloader, sdf_model, vae_model, recon_dir, take_mod...
  function filter_threshold (line 137) | def filter_threshold(mesh, gt_pc, threshold): # mesh is path to mesh wit...
  function extract_latents (line 143) | def extract_latents(test_dataloader, sdf_model, vae_model, save_dir):

FILE: utils/renderer.py
  class OnlineObjectRenderer (line 13) | class OnlineObjectRenderer:
    method __init__ (line 14) | def __init__(self, fov=np.pi / 6, caching=True):
    method _init_scene (line 25) | def _init_scene(self, height=480, width=480):
    method add_mesh (line 34) | def add_mesh(self, path, name, rotation=None, translation=None):
    method add_pointcloud (line 58) | def add_pointcloud(self, path, name, colors=None, rotation=None, trans...
    method clear (line 85) | def clear(self):
    method render (line 90) | def render(self):

FILE: utils/tmd.py
  function process_one (line 9) | def process_one(shape_dir):
  function tmd_from_pcs (line 28) | def tmd_from_pcs(gen_pcs):
  function Total_Mutual_Difference (line 40) | def Total_Mutual_Difference(args):
  function main (line 56) | def main():

FILE: utils/uhd.py
  function directed_hausdorff (line 11) | def directed_hausdorff(point_cloud1:torch.Tensor, point_cloud2:torch.Ten...
  function nn_distance (line 38) | def nn_distance(query_points, ref_points):
  function completeness (line 44) | def completeness(query_points, ref_points, thres=0.03):
  function process_one (line 50) | def process_one(shape_dir):
  function uhd_from_pcs (line 86) | def uhd_from_pcs(gen_pcs, partial_pc):
  function func (line 106) | def func(args):
  function main (line 119) | def main():

FILE: utils/visualize.py
  function create_point_marker (line 7) | def create_point_marker(center, color):
  function get_color (line 17) | def get_color(labels):
  function vis_pc (line 21) | def vis_pc(obj_pc):
  function main (line 27) | def main(mesh, pc, query):