https://github.com/graphdeco-inria/gaussian-splatting/issues/719#issue-2198958106 Hello, I have been working on 3D reconstruction using 3DGS recently. 3DGS can also be used to reconstruct with nerf datasets. However, I have been facing continuous reconstruction failures when using a public dataset SPEED+. Therefore, I would like to consult with you about the correctness of my camera pose transformation. I have encountered issues with the transformed SPEED+ dataset when training with nerf as well.

import json import math import numpy as np

Read the camera intrinsic parameters file.

with open('camera.json', 'r') as f: camera_params = json.load(f)

Read the camera extrinsic parameters file.

with open(r'G:\speed+\shirtv1\roe2\roe2.json', 'r') as f: extrinsic_params = json.load(f)

Extracting Camera Intrinsic Matrix

camera_matrix = np.array(camera_params['cameraMatrix']) dist_coeffs = np.array(camera_params['distCoeffs'])

calculate camera_angle_x

Nu = camera_params["Nu"] fx = camera_params["fx"] camera_angle_x = 2 math.atan(Nu / (2 fx))

Create storage for storing Nerf format data.

frames = []

def quaternion_to_rotation_matrix(q): q = np.array(q) r = np.array([[1 - 2 (q[2] 2 + q[3] 2), 2 (q[1] q[2] - q[0] q[3]), 2 (q[1] q[3] + q[0] q[2])], [2 (q[1] q[2] + q[0] q[3]), 1 - 2 (q[1] 2 + q[3] 2), 2 (q[2] q[3] - q[0] q[1])], [2 (q[1] q[3] - q[0] q[2]), 2 (q[2] q[3] + q[0] q[1]), 1 - 2 * (q[1] 2 + q[2] 2)]]) return r

for param in extrinsic_params: filename = param['filename'] q_vbs2tango_true = param['q_vbs2tango_true'] r_Vo2To_vbs_true = param['r_Vo2To_vbs_true']

# Create a rotation matrix 
rotation_matrix = np.eye(4)
rotation_matrix[:3, :3] = quaternion_to_rotation_matrix(q_vbs2tango_true)

# Construct a translation vector 
translation_vector = np.array(r_Vo2To_vbs_true).reshape(3, 1)

# Build a transformation matrix 
transform_matrix = np.eye(4)
transform_matrix[:3, :3] = rotation_matrix[:3, :3]
transform_matrix[:3, 3] = translation_vector.squeeze()

frame = {
    'file_path': filename,
    "rotation": camera_angle_x,
    'transform_matrix': transform_matrix.tolist()
}
frames.append(frame)

Prepare data in Nerf format

nerf_data = { 'camera_angle_x': float(camera_params['ppx']), "rotation": frame["rotation"], 'frames': frames }

Write the data to a file

with open(r'G:\speed+\shirtv1\roe2\lightbox\roe1_to_nerf.json', 'w') as f: json.dump(nerf_data, f, indent=4)

maximeraafat / BlenderNeRF

Failed to reconstruct with SPEED+ dataset #35