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Objaverse Point Cloud Dataset Loader
Load pre-processed .pt files.
Each .pt file is assumed to be a dictionary of PyTorch tensors.
"""
import os
import torch
import numpy as np
import open3d as o3d
from PIL import Image
import json
from collections.abc import Sequence
import random
from .builder import DATASETS
from .defaults import DefaultDataset, DefaultImagePointDataset
@DATASETS.register_module()
class Cap3DDataset(DefaultDataset):
"""
Cap3D Dataset for loading .pt files.
"""
def __init__(
self,
data_num=None,
**kwargs,
):
super().__init__(**kwargs)
if data_num:
self.data_list = self.data_list[:data_num]
@staticmethod
def get_normals(center, coords):
Cs = np.repeat(center.reshape((1, -1)), coords.shape[0], axis=0)
view_dirs = coords - Cs
view_dirs = view_dirs / np.linalg.norm(view_dirs, axis=-1, keepdims=True)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(coords)
pcd.estimate_normals()
normals = np.asarray(pcd.normals)
dot_product = np.sum(normals * view_dirs, axis=-1)
flip_mask = dot_product < 0
normals[flip_mask] = -normals[flip_mask]
# Normalize normals a nd m
normals = normals / np.linalg.norm(normals, axis=-1, keepdims=True)
return normals
def get_data(self, idx):
"""
The core data loading function.
Args:
idx (int): The index of the data sample.
Returns:
dict: A dictionary containing the loaded and processed point cloud data.
"""
data_path = self.data_list[idx % len(self.data_list)]
name = self.get_data_name(idx)
split = self.get_split_name(idx)
try:
data = torch.load(data_path, map_location="cpu")
except Exception as e:
raise IOError(f"Failed to load a .pt file at {data_path}. Error: {e}")
data = data.numpy().transpose()
color = data[:, 3:]
coord = data[:, :3]
normal = self.get_normals(np.mean(coord, axis=0), coord)
data_dict = {}
data_dict["coord"] = coord
data_dict["color"] = color
data_dict["normal"] = normal
data_dict["name"] = name
data_dict["split"] = split
return data_dict
@DATASETS.register_module()
class Cap3DImagePointDataset(DefaultImagePointDataset):
def __init__(
self,
data_num=-1,
**kwargs,
):
self.data_num = data_num
super().__init__(**kwargs)
def get_data_list(self):
split_list = {}
if isinstance(self.split, str):
data_path = os.path.join(self.data_root, "splits", f"{self.split}.json")
with open(data_path, "r", encoding="utf-8") as file:
data_list = json.load(file)
split_list[self.split] = list(data_list.keys())
elif isinstance(self.split, Sequence):
data_list = {}
for split in self.split:
data_path = os.path.join(self.data_root, "splits", f"{split}.json")
with open(data_path, "r", encoding="utf-8") as file:
data_split_dict = json.load(file)
data_list.update(data_split_dict)
split_list[split] = list(data_split_dict.keys())
else:
raise NotImplementedError
if self.data_num < 0 or self.data_num >= len(data_list):
selected_data_list = data_list
selected_split_list = split_list
return selected_data_list, selected_split_list
all_keys = list(data_list.keys())
selected_keys = random.sample(all_keys, self.data_num)
selected_data_list = {key: data_list[key] for key in selected_keys}
valid_keys = set(selected_keys)
selected_split_list = {}
for split_name, key_list in split_list.items():
filtered_keys = [key for key in key_list if key in valid_keys]
if filtered_keys:
selected_split_list[split_name] = filtered_keys
return selected_data_list, selected_split_list
@staticmethod
def get_normals(center, coords):
Cs = np.repeat(center.reshape((1, -1)), coords.shape[0], axis=0)
view_dirs = coords - Cs
view_dirs = view_dirs / np.linalg.norm(view_dirs, axis=-1, keepdims=True)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(coords)
pcd.estimate_normals()
normals = np.asarray(pcd.normals)
dot_product = np.sum(normals * view_dirs, axis=-1)
flip_mask = dot_product < 0
normals[flip_mask] = -normals[flip_mask]
normals = normals / np.linalg.norm(normals, axis=-1, keepdims=True)
return normals
def get_pc_data(self, data_path):
try:
data = torch.load(data_path, map_location="cpu")
except Exception as e:
raise IOError(f"Failed to load a .pt file at {data_path}. Error: {e}")
data = data.numpy().transpose()
color = data[:, 3:]
coord = data[:, :3]
normal = self.get_normals(np.mean(coord, axis=0), coord)
data_dict = {}
data_dict["coord"] = coord
data_dict["color"] = color
data_dict["normal"] = normal
return data_dict
def get_data(self, idx):
data_dict = {}
name = self.get_data_name(idx)
split = self.get_split_name(idx)
data_dict["name"] = name
data_dict["split"] = split
data_path = self.data_list[name]
pointclouds_path = data_path["pointclouds"]
pc_dict = self.get_pc_data(pointclouds_path)
data_dict.update(pc_dict)
imgs_path = data_path["images"]
imgs = [Image.open(asset).convert("RGB") for asset in imgs_path]
if len(imgs) > 0:
img_width, img_height = imgs[0].size
div_w = img_width // self.patch_w
div_h = img_height // self.patch_h
div_min = max(min(div_w, div_h), 1)
crop_img_width = div_min * self.patch_w
crop_img_height = div_min * self.patch_h
left = int((img_width - crop_img_width) / 2)
top = int((img_height - crop_img_height) / 2)
right = int((img_width + crop_img_width) / 2)
bottom = int((img_height + crop_img_height) / 2)
imgs = [img.crop((left, top, right, bottom)) for img in imgs]
imgs = [self.transform_img(img) for img in imgs]
imgs_list = torch.stack(imgs)
data_dict["images"] = imgs_list.float()
else:
data_dict["images"] = torch.empty(
(0, 3, self.patch_h * self.patch_size, self.patch_w * self.patch_size)
)
data_dict["img_num"] = np.array([data_dict["images"].shape[0]], dtype=np.int32)
correspondences_path = data_path["correspondences"]
correspondence_infos = np.ones(
(data_dict["coord"].shape[0], len(correspondences_path), 2),
dtype=np.float32,
) * (-1)
for asset_id, asset in enumerate(correspondences_path):
correspondence_info = np.load(asset).astype(np.float32)
if np.array_equal(correspondence_info, -np.ones((1, 3))):
continue
correspondence_info = self.resize_correspondence_info(
correspondence_info,
(self.patch_h * self.patch_size, self.patch_w * self.patch_size),
(img_height, img_width),
(left, top, right, bottom),
self.patch_size,
)
correspondence_infos[
correspondence_info[:, -1].astype(np.int32), asset_id, :
] = correspondence_info[:, :-1]
data_dict["correspondence"] = correspondence_infos
if "coord" in data_dict.keys():
data_dict["coord"] = data_dict["coord"].astype(np.float32)
if "color" in data_dict.keys():
data_dict["color"] = data_dict["color"].astype(np.float32)
if "normal" in data_dict.keys():
data_dict["normal"] = data_dict["normal"].astype(np.float32)
if "segment" in data_dict.keys():
data_dict["segment"] = data_dict["segment"].reshape([-1]).astype(np.int32)
else:
data_dict["segment"] = (
np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
)
if "instance" in data_dict.keys():
data_dict["instance"] = data_dict["instance"].reshape([-1]).astype(np.int32)
else:
data_dict["instance"] = (
np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
)
return data_dict
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