diff --git a/vggt_to_colmap.py b/vggt_to_colmap.py
new file mode 100644
index 0000000..c288380
--- /dev/null
+++ b/vggt_to_colmap.py
@@ -0,0 +1,595 @@
+import os
+import argparse
+import numpy as np
+import torch
+import glob
+import struct
+from scipy.spatial.transform import Rotation
+import sys
+from PIL import Image
+import cv2
+import requests
+import tempfile
+
+sys.path.append("vggt/")
+
+from vggt.models.vggt import VGGT
+from vggt.utils.load_fn import load_and_preprocess_images
+from vggt.utils.pose_enc import pose_encoding_to_extri_intri
+from vggt.utils.geometry import unproject_depth_map_to_point_map
+
+def load_model(device=None):
+    """Load and initialize the VGGT model."""
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using device: {device}")
+    
+    model = VGGT.from_pretrained("facebook/VGGT-1B")
+
+    # model = VGGT()
+    # _URL = "https://huggingface.co/facebook/VGGT-1B/resolve/main/model.pt"
+    # model.load_state_dict(torch.hub.load_state_dict_from_url(_URL))
+    
+    model.eval()
+    model = model.to(device)
+    return model, device
+
+def process_images(image_dir, model, device):
+    """Process images with VGGT and return predictions."""
+    image_names = glob.glob(os.path.join(image_dir, "*"))
+    image_names = sorted([f for f in image_names if f.lower().endswith(('.png', '.jpg', '.jpeg'))])
+    print(f"Found {len(image_names)} images")
+    
+    if len(image_names) == 0:
+        raise ValueError(f"No images found in {image_dir}")
+
+    original_images = []
+    for img_path in image_names:
+        img = Image.open(img_path).convert('RGB')
+        original_images.append(np.array(img))
+    
+    images = load_and_preprocess_images(image_names).to(device)
+    print(f"Preprocessed images shape: {images.shape}")
+    
+    print("Running inference...")
+    dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+    
+    with torch.no_grad():
+        with torch.cuda.amp.autocast(dtype=dtype):
+            predictions = model(images)
+
+    print("Converting pose encoding to camera parameters...")
+    extrinsic, intrinsic = pose_encoding_to_extri_intri(predictions["pose_enc"], images.shape[-2:])
+    predictions["extrinsic"] = extrinsic
+    predictions["intrinsic"] = intrinsic
+
+    for key in predictions.keys():
+        if isinstance(predictions[key], torch.Tensor):
+            predictions[key] = predictions[key].cpu().numpy().squeeze(0)  # remove batch dimension
+    
+    print("Computing 3D points from depth maps...")
+    depth_map = predictions["depth"]  # (S, H, W, 1)
+    world_points = unproject_depth_map_to_point_map(depth_map, predictions["extrinsic"], predictions["intrinsic"])
+    predictions["world_points_from_depth"] = world_points
+    
+    predictions["original_images"] = original_images
+    
+    S, H, W = world_points.shape[:3]
+    normalized_images = np.zeros((S, H, W, 3), dtype=np.float32)
+    
+    for i, img in enumerate(original_images):
+        resized_img = cv2.resize(img, (W, H))
+        normalized_images[i] = resized_img / 255.0
+    
+    predictions["images"] = normalized_images
+    
+    return predictions, image_names
+
+def extrinsic_to_colmap_format(extrinsics):
+    """Convert extrinsic matrices to COLMAP format (quaternion + translation)."""
+    num_cameras = extrinsics.shape[0]
+    quaternions = []
+    translations = []
+    
+    for i in range(num_cameras):
+        # VGGT's extrinsic is camera-to-world (R|t) format
+        R = extrinsics[i, :3, :3]
+        t = extrinsics[i, :3, 3]
+        
+        # Convert rotation matrix to quaternion
+        # COLMAP quaternion format is [qw, qx, qy, qz]
+        rot = Rotation.from_matrix(R)
+        quat = rot.as_quat()  # scipy returns [x, y, z, w]
+        quat = np.array([quat[3], quat[0], quat[1], quat[2]])  # Convert to [w, x, y, z]
+        
+        quaternions.append(quat)
+        translations.append(t)
+    
+    return np.array(quaternions), np.array(translations)
+
+def download_file_from_url(url, filename):
+    """Downloads a file from a URL, handling redirects."""
+    try:
+        response = requests.get(url, allow_redirects=False)
+        response.raise_for_status() 
+
+        if response.status_code == 302:  
+            redirect_url = response.headers["Location"]
+            response = requests.get(redirect_url, stream=True)
+            response.raise_for_status()
+        else:
+            response = requests.get(url, stream=True)
+            response.raise_for_status()
+
+        with open(filename, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        print(f"Downloaded {filename} successfully.")
+        return True
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error downloading file: {e}")
+        return False
+
+def segment_sky(image_path, onnx_session, mask_filename=None):
+    """
+    Segments sky from an image using an ONNX model.
+    """
+    image = cv2.imread(image_path)
+
+    result_map = run_skyseg(onnx_session, [320, 320], image)
+    result_map_original = cv2.resize(result_map, (image.shape[1], image.shape[0]))
+
+    # Fix: Invert the mask so that 255 = non-sky, 0 = sky
+    # The model outputs low values for sky, high values for non-sky
+    output_mask = np.zeros_like(result_map_original)
+    output_mask[result_map_original < 32] = 255  # Use threshold of 32
+
+    if mask_filename is not None:
+        os.makedirs(os.path.dirname(mask_filename), exist_ok=True)
+        cv2.imwrite(mask_filename, output_mask)
+    
+    return output_mask
+
+def run_skyseg(onnx_session, input_size, image):
+    """
+    Runs sky segmentation inference using ONNX model.
+    """
+    import copy
+    
+    temp_image = copy.deepcopy(image)
+    resize_image = cv2.resize(temp_image, dsize=(input_size[0], input_size[1]))
+    x = cv2.cvtColor(resize_image, cv2.COLOR_BGR2RGB)
+    x = np.array(x, dtype=np.float32)
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+    x = (x / 255 - mean) / std
+    x = x.transpose(2, 0, 1)
+    x = x.reshape(-1, 3, input_size[0], input_size[1]).astype("float32")
+
+    input_name = onnx_session.get_inputs()[0].name
+    output_name = onnx_session.get_outputs()[0].name
+    onnx_result = onnx_session.run([output_name], {input_name: x})
+
+    onnx_result = np.array(onnx_result).squeeze()
+    min_value = np.min(onnx_result)
+    max_value = np.max(onnx_result)
+    onnx_result = (onnx_result - min_value) / (max_value - min_value)
+    onnx_result *= 255
+    onnx_result = onnx_result.astype("uint8")
+
+    return onnx_result
+
+def filter_and_prepare_points(predictions, conf_threshold, mask_sky=False, mask_black_bg=False, 
+                             mask_white_bg=False, stride=1, prediction_mode="Depthmap and Camera Branch"):
+    """
+    Filter points based on confidence and prepare for COLMAP format.
+    Implementation matches the conventions in the original VGGT code.
+    """
+    
+    if "Pointmap" in prediction_mode:
+        print("Using Pointmap Branch")
+        if "world_points" in predictions:
+            pred_world_points = predictions["world_points"]
+            pred_world_points_conf = predictions.get("world_points_conf", np.ones_like(pred_world_points[..., 0]))
+        else:
+            print("Warning: world_points not found in predictions, falling back to depth-based points")
+            pred_world_points = predictions["world_points_from_depth"]
+            pred_world_points_conf = predictions.get("depth_conf", np.ones_like(pred_world_points[..., 0]))
+    else:
+        print("Using Depthmap and Camera Branch")
+        pred_world_points = predictions["world_points_from_depth"]
+        pred_world_points_conf = predictions.get("depth_conf", np.ones_like(pred_world_points[..., 0]))
+
+    colors_rgb = predictions["images"] 
+    
+    S, H, W = pred_world_points.shape[:3]
+    if colors_rgb.shape[:3] != (S, H, W):
+        print(f"Reshaping colors_rgb from {colors_rgb.shape} to match {(S, H, W, 3)}")
+        reshaped_colors = np.zeros((S, H, W, 3), dtype=np.float32)
+        for i in range(S):
+            if i < len(colors_rgb):
+                reshaped_colors[i] = cv2.resize(colors_rgb[i], (W, H))
+        colors_rgb = reshaped_colors
+    
+    colors_rgb = (colors_rgb * 255).astype(np.uint8)
+    
+    if mask_sky:
+        print("Applying sky segmentation mask")
+        try:
+            import onnxruntime
+         
+            with tempfile.TemporaryDirectory() as temp_dir:
+                print(f"Created temporary directory for sky segmentation: {temp_dir}")
+                temp_images_dir = os.path.join(temp_dir, "images")
+                sky_masks_dir = os.path.join(temp_dir, "sky_masks")
+                os.makedirs(temp_images_dir, exist_ok=True)
+                os.makedirs(sky_masks_dir, exist_ok=True)
+                
+                image_list = []
+                for i, img in enumerate(colors_rgb):
+                    img_path = os.path.join(temp_images_dir, f"image_{i:04d}.png")
+                    image_list.append(img_path)
+                    cv2.imwrite(img_path, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+                
+           
+                skyseg_path = os.path.join(temp_dir, "skyseg.onnx")
+                if not os.path.exists("skyseg.onnx"): 
+                    print("Downloading skyseg.onnx...")
+                    download_success = download_file_from_url(
+                        "https://huggingface.co/JianyuanWang/skyseg/resolve/main/skyseg.onnx", 
+                        skyseg_path
+                    )
+                    if not download_success:
+                        print("Failed to download skyseg model, skipping sky filtering")
+                        mask_sky = False
+                else:
+            
+                    import shutil
+                    shutil.copy("skyseg.onnx", skyseg_path)
+                
+                if mask_sky:  
+                    skyseg_session = onnxruntime.InferenceSession(skyseg_path)
+                    sky_mask_list = []
+                    
+                    for img_path in image_list:
+                        mask_path = os.path.join(sky_masks_dir, os.path.basename(img_path))
+                        sky_mask = segment_sky(img_path, skyseg_session, mask_path)
+           
+                        if sky_mask.shape[0] != H or sky_mask.shape[1] != W:
+                            sky_mask = cv2.resize(sky_mask, (W, H))
+                        
+                        sky_mask_list.append(sky_mask)
+                    
+                    sky_mask_array = np.array(sky_mask_list)
+                    
+                    sky_mask_binary = (sky_mask_array > 0.1).astype(np.float32)
+                    pred_world_points_conf = pred_world_points_conf * sky_mask_binary
+                    print(f"Applied sky mask, shape: {sky_mask_binary.shape}")
+                
+        except (ImportError, Exception) as e:
+            print(f"Error in sky segmentation: {e}")
+            mask_sky = False
+    
+    vertices_3d = pred_world_points.reshape(-1, 3)
+    conf = pred_world_points_conf.reshape(-1)
+    colors_rgb_flat = colors_rgb.reshape(-1, 3)
+
+    
+
+    if len(conf) != len(colors_rgb_flat):
+        print(f"WARNING: Shape mismatch between confidence ({len(conf)}) and colors ({len(colors_rgb_flat)})")
+        min_size = min(len(conf), len(colors_rgb_flat))
+        conf = conf[:min_size]
+        vertices_3d = vertices_3d[:min_size]
+        colors_rgb_flat = colors_rgb_flat[:min_size]
+    
+    if conf_threshold == 0.0:
+        conf_thres_value = 0.0
+    else:
+        conf_thres_value = np.percentile(conf, conf_threshold)
+    
+    print(f"Using confidence threshold: {conf_threshold}% (value: {conf_thres_value:.4f})")
+    conf_mask = (conf >= conf_thres_value) & (conf > 1e-5)
+    
+    if mask_black_bg:
+        print("Filtering black background")
+        black_bg_mask = colors_rgb_flat.sum(axis=1) >= 16
+        conf_mask = conf_mask & black_bg_mask
+    
+    if mask_white_bg:
+        print("Filtering white background")
+        white_bg_mask = ~((colors_rgb_flat[:, 0] > 240) & (colors_rgb_flat[:, 1] > 240) & (colors_rgb_flat[:, 2] > 240))
+        conf_mask = conf_mask & white_bg_mask
+    
+    filtered_vertices = vertices_3d[conf_mask]
+    filtered_colors = colors_rgb_flat[conf_mask]
+    
+    if len(filtered_vertices) == 0:
+        print("Warning: No points remaining after filtering. Using default point.")
+        filtered_vertices = np.array([[0, 0, 0]])
+        filtered_colors = np.array([[200, 200, 200]])
+    
+    print(f"Filtered to {len(filtered_vertices)} points")
+    
+    points3D = []
+    point_indices = {}
+    image_points2D = [[] for _ in range(len(pred_world_points))]
+    
+    print(f"Preparing points for COLMAP format with stride {stride}...")
+    
+    total_points = 0
+    for img_idx in range(S):
+        for y in range(0, H, stride):
+            for x in range(0, W, stride):
+                flat_idx = img_idx * H * W + y * W + x
+                
+                if flat_idx >= len(conf):
+                    continue
+                
+                if conf[flat_idx] < conf_thres_value or conf[flat_idx] <= 1e-5:
+                    continue
+                
+                if mask_black_bg and colors_rgb_flat[flat_idx].sum() < 16:
+                    continue
+                
+                if mask_white_bg and all(colors_rgb_flat[flat_idx] > 240):
+                    continue
+                
+                point3D = vertices_3d[flat_idx]
+                rgb = colors_rgb_flat[flat_idx]
+                
+                if not np.all(np.isfinite(point3D)):
+                    continue
+                
+                point_hash = hash_point(point3D, scale=100)
+                
+                if point_hash not in point_indices:
+                    point_idx = len(points3D)
+                    point_indices[point_hash] = point_idx
+                    
+                    point_entry = {
+                        "id": point_idx,
+                        "xyz": point3D,
+                        "rgb": rgb,
+                        "error": 1.0,
+                        "track": [(img_idx, len(image_points2D[img_idx]))]
+                    }
+                    points3D.append(point_entry)
+                    total_points += 1
+                else:
+                    point_idx = point_indices[point_hash]
+                    points3D[point_idx]["track"].append((img_idx, len(image_points2D[img_idx])))
+                
+                image_points2D[img_idx].append((x, y, point_indices[point_hash]))
+    
+    print(f"Prepared {len(points3D)} 3D points with {sum(len(pts) for pts in image_points2D)} observations for COLMAP")
+    return points3D, image_points2D
+
+def hash_point(point, scale=100):
+    """Create a hash for a 3D point by quantizing coordinates."""
+    quantized = tuple(np.round(point * scale).astype(int))
+    return hash(quantized)
+
+def write_colmap_cameras_txt(file_path, intrinsics, image_width, image_height):
+    """Write camera intrinsics to COLMAP cameras.txt format."""
+    with open(file_path, 'w') as f:
+        f.write("# Camera list with one line of data per camera:\n")
+        f.write("#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n")
+        f.write(f"# Number of cameras: {len(intrinsics)}\n")
+        
+        for i, intrinsic in enumerate(intrinsics):
+            camera_id = i + 1  # COLMAP uses 1-indexed camera IDs
+            model = "PINHOLE" 
+            
+            fx = intrinsic[0, 0]
+            fy = intrinsic[1, 1]
+            cx = intrinsic[0, 2]
+            cy = intrinsic[1, 2]
+            
+            f.write(f"{camera_id} {model} {image_width} {image_height} {fx} {fy} {cx} {cy}\n")
+
+def write_colmap_images_txt(file_path, quaternions, translations, image_points2D, image_names):
+    """Write camera poses and keypoints to COLMAP images.txt format."""
+    with open(file_path, 'w') as f:
+        f.write("# Image list with two lines of data per image:\n")
+        f.write("#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n")
+        f.write("#   POINTS2D[] as (X, Y, POINT3D_ID)\n")
+        
+        num_points = sum(len(points) for points in image_points2D)
+        avg_points = num_points / len(image_points2D) if image_points2D else 0
+        f.write(f"# Number of images: {len(quaternions)}, mean observations per image: {avg_points:.1f}\n")
+        
+        for i in range(len(quaternions)):
+            image_id = i + 1 
+            camera_id = i + 1  
+          
+            qw, qx, qy, qz = quaternions[i]
+            tx, ty, tz = translations[i]
+            
+            f.write(f"{image_id} {qw} {qx} {qy} {qz} {tx} {ty} {tz} {camera_id} {os.path.basename(image_names[i])}\n")
+            
+            points_line = " ".join([f"{x} {y} {point3d_id+1}" for x, y, point3d_id in image_points2D[i]])
+            f.write(f"{points_line}\n")
+
+def write_colmap_points3D_txt(file_path, points3D):
+    """Write 3D points and tracks to COLMAP points3D.txt format."""
+    with open(file_path, 'w') as f:
+        f.write("# 3D point list with one line of data per point:\n")
+        f.write("#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n")
+        
+        avg_track_length = sum(len(point["track"]) for point in points3D) / len(points3D) if points3D else 0
+        f.write(f"# Number of points: {len(points3D)}, mean track length: {avg_track_length:.4f}\n")
+        
+        for point in points3D:
+            point_id = point["id"] + 1  
+            x, y, z = point["xyz"]
+            r, g, b = point["rgb"]
+            error = point["error"]
+            
+            track = " ".join([f"{img_id+1} {point2d_idx}" for img_id, point2d_idx in point["track"]])
+            
+            f.write(f"{point_id} {x} {y} {z} {int(r)} {int(g)} {int(b)} {error} {track}\n")
+
+def write_colmap_cameras_bin(file_path, intrinsics, image_width, image_height):
+    """Write camera intrinsics to COLMAP cameras.bin format."""
+    with open(file_path, 'wb') as fid:
+        # Write number of cameras (uint64)
+        fid.write(struct.pack('<Q', len(intrinsics)))
+        
+        for i, intrinsic in enumerate(intrinsics):
+            camera_id = i + 1
+            model_id = 1 
+            
+            fx = float(intrinsic[0, 0])
+            fy = float(intrinsic[1, 1])
+            cx = float(intrinsic[0, 2])
+            cy = float(intrinsic[1, 2])
+            
+            # Camera ID (uint32)
+            fid.write(struct.pack('<I', camera_id))
+            # Model ID (uint32)
+            fid.write(struct.pack('<I', model_id))
+            # Width (uint64)
+            fid.write(struct.pack('<Q', image_width))
+            # Height (uint64)
+            fid.write(struct.pack('<Q', image_height))
+            
+            # Parameters (double)
+            fid.write(struct.pack('<dddd', fx, fy, cx, cy))
+
+def write_colmap_images_bin(file_path, quaternions, translations, image_points2D, image_names):
+    """Write camera poses and keypoints to COLMAP images.bin format."""
+    with open(file_path, 'wb') as fid:
+        # Write number of images (uint64)
+        fid.write(struct.pack('<Q', len(quaternions)))
+        
+        for i in range(len(quaternions)):
+            image_id = i + 1
+            camera_id = i + 1
+            
+            qw, qx, qy, qz = quaternions[i].astype(float)
+            tx, ty, tz = translations[i].astype(float)
+            
+            image_name = os.path.basename(image_names[i]).encode()
+            points = image_points2D[i]
+            
+            # Image ID (uint32)
+            fid.write(struct.pack('<I', image_id))
+            # Quaternion (double): qw, qx, qy, qz
+            fid.write(struct.pack('<dddd', qw, qx, qy, qz))
+            # Translation (double): tx, ty, tz
+            fid.write(struct.pack('<ddd', tx, ty, tz))
+            # Camera ID (uint32)
+            fid.write(struct.pack('<I', camera_id))
+            # Image name
+            fid.write(struct.pack('<I', len(image_name)))
+            fid.write(image_name)
+            
+            # Write number of 2D points (uint64)
+            fid.write(struct.pack('<Q', len(points)))
+            
+            # Write 2D points: x, y, point3D_id
+            for x, y, point3d_id in points:
+                fid.write(struct.pack('<dd', float(x), float(y)))
+                fid.write(struct.pack('<Q', point3d_id + 1))
+
+def write_colmap_points3D_bin(file_path, points3D):
+    """Write 3D points and tracks to COLMAP points3D.bin format."""
+    with open(file_path, 'wb') as fid:
+        # Write number of points (uint64)
+        fid.write(struct.pack('<Q', len(points3D)))
+        
+        for point in points3D:
+            point_id = point["id"] + 1
+            x, y, z = point["xyz"].astype(float)
+            r, g, b = point["rgb"].astype(np.uint8)
+            error = float(point["error"])
+            track = point["track"]
+            
+            # Point ID (uint64)
+            fid.write(struct.pack('<Q', point_id))
+            # Position (double): x, y, z
+            fid.write(struct.pack('<ddd', x, y, z))
+            # Color (uint8): r, g, b
+            fid.write(struct.pack('<BBB', int(r), int(g), int(b)))
+            # Error (double)
+            fid.write(struct.pack('<d', error))
+            
+            # Track: list of (image_id, point2D_idx)
+            fid.write(struct.pack('<Q', len(track)))
+            for img_id, point2d_idx in track:
+                fid.write(struct.pack('<II', img_id + 1, point2d_idx))
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert images to COLMAP format using VGGT")
+    parser.add_argument("--image_dir", type=str, required=True, 
+                        help="Directory containing input images")
+    parser.add_argument("--output_dir", type=str, default="colmap_output", 
+                        help="Directory to save COLMAP files")
+    parser.add_argument("--conf_threshold", type=float, default=50.0, 
+                        help="Confidence threshold (0-100%) for including points")
+    parser.add_argument("--mask_sky", action="store_true",
+                        help="Filter out points likely to be sky")
+    parser.add_argument("--mask_black_bg", action="store_true",
+                        help="Filter out points with very dark/black color")
+    parser.add_argument("--mask_white_bg", action="store_true",
+                        help="Filter out points with very bright/white color")
+    parser.add_argument("--binary", action="store_true", 
+                        help="Output binary COLMAP files instead of text")
+    parser.add_argument("--stride", type=int, default=1, 
+                        help="Stride for point sampling (higher = fewer points)")
+    parser.add_argument("--prediction_mode", type=str, default="Depthmap and Camera Branch",
+                        choices=["Depthmap and Camera Branch", "Pointmap Branch"],
+                        help="Which prediction branch to use")
+    
+    args = parser.parse_args()
+    
+    os.makedirs(args.output_dir, exist_ok=True)
+    
+    model, device = load_model()
+    
+    predictions, image_names = process_images(args.image_dir, model, device)
+    
+    print("Converting camera parameters to COLMAP format...")
+    quaternions, translations = extrinsic_to_colmap_format(predictions["extrinsic"])
+    
+    print(f"Filtering points with confidence threshold {args.conf_threshold}% and stride {args.stride}...")
+    points3D, image_points2D = filter_and_prepare_points(
+        predictions, 
+        args.conf_threshold, 
+        mask_sky=args.mask_sky, 
+        mask_black_bg=args.mask_black_bg,
+        mask_white_bg=args.mask_white_bg,
+        stride=args.stride,
+        prediction_mode=args.prediction_mode
+    )
+    
+    height, width = predictions["depth"].shape[1:3]
+
+    print(f"Writing {'binary' if args.binary else 'text'} COLMAP files to {args.output_dir}...")
+    if args.binary:
+        write_colmap_cameras_bin(
+            os.path.join(args.output_dir, "cameras.bin"), 
+            predictions["intrinsic"], width, height)
+        write_colmap_images_bin(
+            os.path.join(args.output_dir, "images.bin"), 
+            quaternions, translations, image_points2D, image_names)
+        write_colmap_points3D_bin(
+            os.path.join(args.output_dir, "points3D.bin"), 
+            points3D)
+    else:
+        write_colmap_cameras_txt(
+            os.path.join(args.output_dir, "cameras.txt"), 
+            predictions["intrinsic"], width, height)
+        write_colmap_images_txt(
+            os.path.join(args.output_dir, "images.txt"), 
+            quaternions, translations, image_points2D, image_names)
+        write_colmap_points3D_txt(
+            os.path.join(args.output_dir, "points3D.txt"), 
+            points3D)
+    
+    print(f"COLMAP files successfully written to {args.output_dir}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file