tink/nerfstudio
4
0
Fork 0
mirror of https://github.com/nerfstudio-project/nerfstudio.git synced 2026-11-10 18:17:31 +08:00
Code

Add pointcloud loading for blender parser (#2902)

Browse Source 
* Add pointcloud loading for blender parser

* Fix argument

* Fix docstring

* Fix metadata in dataparsers

* Add changes from review

* Avoid changing default behavior

* Apply `scale_factor` to loaded points

---------

Co-authored-by: Brent Yi <yibrenth@gmail.com>
This commit is contained in:
Gunjan Chhablani 2024-04-11 16:41:14 -04:00 committed by GitHub
parent 03bdeff2ad
commit c302cc5301
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 22 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
nerfstudio/data/dataparsers/blender_dataparser.py
View File

@ -17,10 +17,11 @@ from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
from typing import Type
from typing import Optional, Type
import imageio
import numpy as np
import open3d as o3d
import torch
from nerfstudio.cameras.cameras import Cameras, CameraType
@ -42,6 +43,9 @@ class BlenderDataParserConfig(DataParserConfig):
"""How much to scale the camera origins by."""
alpha_color: str = "white"
"""alpha color of background"""
ply_path: Optional[Path] = None
"""Path to PLY file to load 3D points from, defined relative to the dataset directory. This is helpful for
Gaussian splatting and generally unused otherwise. If `None`, points are initialized randomly."""
@dataclass
@ -94,12 +98,29 @@ class Blender(DataParser):
camera_type=CameraType.PERSPECTIVE,
)
metadata = {}
if self.config.ply_path is not None:
metadata.update(self._load_3D_points(self.config.data / self.config.ply_path))
dataparser_outputs = DataparserOutputs(
image_filenames=image_filenames,
cameras=cameras,
alpha_color=self.alpha_color_tensor,
scene_box=scene_box,
dataparser_scale=self.scale_factor,
metadata=metadata,
)
return dataparser_outputs
def _load_3D_points(self, ply_file_path: Path):
pcd = o3d.io.read_point_cloud(str(ply_file_path))
points3D = torch.from_numpy(np.asarray(pcd.points, dtype=np.float32) * self.config.scale_factor)
points3D_rgb = torch.from_numpy((np.asarray(pcd.colors) * 255).astype(np.uint8))
out = {
"points3D_xyz": points3D,
"points3D_rgb": points3D_rgb,
}
return out