code: def read_point_cloud(points_txt_path):
      with open(points_txt_path, "r") as fid:
          line = fid.readline()
          assert (
              line
              == "# 3D point list with one line of data per point:\n"
          )
          line = fid.readline()
          assert (
              line
              == "#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n"
          )
          line = fid.readline()
          assert re.search(
              "^# Number of points: \d+, mean track length: [-+]?\d*\.\d+|\d+\n$",
              line,
          )
          num_points, mean_track_length = re.findall(
              r"[-+]?\d*\.\d+|\d+", line
          )
          num_points = int(num_points)
          mean_track_length = float(mean_track_length)
  
          xyz = np.zeros((num_points, 3), dtype=np.float32)
          rgb = np.zeros((num_points, 3), dtype=np.float32)
  
          for i in tqdm(range(num_points), desc="reading point cloud"):
              elems = fid.readline().split()
              xyz[i] = list(map(float, elems[1:4]))
              rgb[i] = list(map(float, elems[4:7]))
          pcd = np.concatenate([xyz, rgb], axis=1)
      return pcd
  
file_path: ml-neuman/data_io/colmap_helper.py
imports: import os
  import re
  from collections import namedtuple
  import numpy
  from tqdm import tqdm
  from geometry.basics import Translation, Rotation
  from cameras.camera_pose import CameraPose
  from cameras.pinhole_camera import PinholeCamera
  from cameras import captures
  from scenes import scene
label_desc: Use image node_id 3 for calling the ColmapAsciiReader obj's underlying member method code with example usage: obj.read_point_cloud(points_txt_path) and returns: pcd