3DGS本身支持对Blender数据集的训练，其主要数据格式为：

<location>
|---images
|   |---<image 0>
|   |---<image 1>
|   |---...
|---transformers_train.json
|---point3d.ply

数据准备

通常我们自己采集的数据集来自激光扫描装置，提供了las格式的点云文件、json格式的相机位姿以及图片。

首先处理las格式的点云文件，需要将其转化为二进制的ply文件。

然后查看transformers_train.json文件，主要是针对性修改3dgs代码文件下/sence/dataset_readers.py这个文件。

def readNerfSyntheticInfo(path, white_background, eval, extension=".jpg"):
    print("Reading Training Transforms")
    train_cam_infos = readCamerasFromTransforms(path, "transforms_train.json", white_background, extension)
    #print("Reading Test Transforms")
    #test_cam_infos = readCamerasFromTransforms(path, "transforms_test.json", white_background, extension)
    
    #if not eval:
    #    train_cam_infos.extend(test_cam_infos)
    #    test_cam_infos = []
    test_cam_infos = []
    nerf_normalization = getNerfppNorm(train_cam_infos)

    ply_path = os.path.join(path, "points3d.ply")
    if not os.path.exists(ply_path):
        # Since this data set has no colmap data, we start with random points
        num_pts = 100_000
        print(f"Generating random point cloud ({num_pts})...")
        
        # We create random points inside the bounds of the synthetic Blender scenes
        xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3
        shs = np.random.random((num_pts, 3)) / 255.0
        pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)))

        storePly(ply_path, xyz, SH2RGB(shs) * 255)
    try:
        pcd = fetchPly(ply_path)
    except:
        pcd = None

    scene_info = SceneInfo(point_cloud=pcd,
                           train_cameras=train_cam_infos,
                           test_cameras=test_cam_infos,
                           nerf_normalization=nerf_normalization,
                           ply_path=ply_path)
    return scene_info

注释掉了Test Transforms相关的部分，不会进行eval，因此不需要test。修改了extension，主要是看自己的图像输入格式。

def readCamerasFromTransforms(path, transformsfile, white_background, extension=".jpg"):
    cam_infos = []

    with open(os.path.join(path, transformsfile)) as json_file:
        contents = json.load(json_file)
        fovx = contents["camera_angle_x"]

        frames = contents["frames"]
        for idx, frame in enumerate(frames):
            cam_name = os.path.join(path,"images", frame["file_path"])

            # NeRF 'transform_matrix' is a camera-to-world transform
            c2w = np.array(frame["transform_matrix"])
            # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward)
            c2w[:3, 1:3] *= -1

            # get the world-to-camera transform and set R, T
            w2c = np.linalg.inv(c2w)
            R = np.transpose(w2c[:3,:3])  # R is stored transposed due to 'glm' in CUDA code
            T = w2c[:3, 3]

            image_path = os.path.join(path, cam_name)
            image_name = Path(cam_name).stem
            image = Image.open(image_path)

            im_data = np.array(image.convert("RGBA"))

            bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0])

            norm_data = im_data / 255.0
            arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4])
            image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB")

            fovy = focal2fov(fov2focal(fovx, image.size[0]), image.size[1])
            FovY = fovy 
            FovX = fovx

            cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
                            image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1]))
            
    return cam_infos

这里通常根据自己的json文件内容更改，几个主要需要注意的点是：

• fovx = contents["camera_angle_x"]

• cam_name = os.path.join(path,"images", frame["file_path"])

def fetchPly(path):
    plydata = PlyData.read(path)
    vertices = plydata['vertex']
    positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T
    colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0
    normals = np.vstack([0,0,0]).T
    return BasicPointCloud(points=positions, colors=colors, normals=normals)

这部分是ply文件相关的，通常自采集数据集不会有点云法向量normals，因此我们根据其colmap部分的代码，也将其设置为0。

一般这样改完，代码就能跑通了，激光点云结合GPS高精度相机位姿，能够解决colmap无法获取相机位姿的问题。