class_docstr: Volumetric TSDF Fusion of RGB-D Images.
      
class_docstr_tok: ["Volumetric","TSDF","Fusion","of","RGB-D","Images","."]
docstr: Convert voxel grid coordinates to world coordinates.
          
docstr_tok: ["Convert","voxel","grid","coordinates","to","world","coordinates","."]
code: def vox2world(vol_origin, vox_coords, vox_size):
      """Convert voxel grid coordinates to world coordinates."""
      vol_origin = vol_origin.astype(np.float32)
      vox_coords = vox_coords.astype(np.float32)
      cam_pts = np.empty_like(vox_coords, dtype=np.float32)
      for i in prange(vox_coords.shape[0]):
          for j in range(3):
              cam_pts[i, j] = vol_origin[j] + (
                  vox_size * vox_coords[i, j]
              )
      return cam_pts
  
file_path: torch-points3d/torch_points3d/datasets/registration/fusion.py
imports: import numpy
  from numba import njit, prange
  from skimage import measure
label_desc: Use image node_id 2 for calling the TSDFVolume obj's underlying member method code with example usage: obj.vox2world(vol_origin, vox_coords, vox_size) and returns: cam_pts