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@@ -33,3 +33,38 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,14 +1,35 @@
 ---
 title: Sat3DGen
-emoji: 🔥
+emoji: 🛰️
 colorFrom: blue
-colorTo: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.13.0
+sdk_version: "4.44.1"
 app_file: app.py
 pinned: false
 license: mit
-short_description: '[ICLR 2026] Sat3DGen: Comprehensive Street-Level 3D Scene Ge'
+models:
+  - qian43/Sat3DGen
+suggested_hardware: t4-small
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Sat3DGen – Street-Level 3D Scene Generation from Satellite Images
+
+**[ICLR 2026]** Generate street-level 3D scenes from a single satellite image.
+
+- 📄 [Paper (OpenReview)](https://openreview.net/forum?id=E7JzkZCofa)
+- 🌐 [Project Page](https://qianmingduowan.github.io/Sat3DGen_project_page/)
+- 💻 [GitHub](https://github.com/qianmingduowan/Sat3DGen)
+- 🤗 [Model](https://huggingface.co/qian43/Sat3DGen)
+
+### Features
+
+- **3D Mesh Generation**: Upload a satellite image → get a downloadable `.obj` mesh with in-browser 3D preview.
+- **Video Rendering**: Select a satellite image and sky panorama → render a walkthrough video along a trajectory.
+
+### Usage
+
+Upload a satellite image or select one from the examples, then click "Generate".
+The model weights are loaded automatically from HuggingFace.
+
+> **Note**: GPU hardware is recommended for reasonable inference speed.

app.py

@@ -0,0 +1,724 @@
+"""Sat3DGen Gradio Demo.
+
+Two-step interactive demo:
+  1. Upload a satellite image -> generate and visualize a 3D mesh.
+  2. Select a demo image with a pre-generated trajectory -> render panorama + perspective video.
+"""
+
+import csv
+import os
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import cv2
+import gradio as gr
+import numpy as np
+import open3d as o3d
+import torch
+import torchvision.transforms as T
+import trimesh
+from PIL import Image
+
+from source.generator import Sat3DGen
+from source.rendering.transform_perspective import compose_rotmat
+
+# ---------------------------------------------------------------------------
+# Global state
+# ---------------------------------------------------------------------------
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+MODEL: Optional[Sat3DGen] = None
+PATCH_SIZE: int = 16
+SAT_TRANSFORM = None
+RESULTS_DIR = Path("./results/gradio_demo")
+TRAJECTORY_PREVIEW_SIZE = 256
+DEFAULT_SKY_FILENAMES = (
+    "default_panorama.jpg",
+    "default_panorama.png",
+    "default_panorama.jpeg",
+    "default_demo_panorama.jpg",
+    "default_demo_panorama.png",
+    "default_demo_panorama.jpeg",
+    "default_sky.jpg",
+    "default_sky.png",
+    "default_sky.jpeg",
+)
+RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+
+
+HUGGINGFACE_REPO = "qian43/Sat3DGen"
+
+def load_model(checkpoint_path: str = "checkpoints"):
+    """Load the Sat3DGen model (singleton).
+
+    Resolution order:
+      1. Local *checkpoint_path* directory (if it contains model files).
+      2. HuggingFace Hub repo ``qian43/Sat3DGen``.
+
+    When loading from a full checkpoint (local or Hub), the backbone
+    weights are already included in the safetensors file, so the
+    standalone DINOv3 download is skipped automatically.
+    """
+    global MODEL, PATCH_SIZE, SAT_TRANSFORM
+
+    if MODEL is not None:
+        return
+
+    model_path: str | None = None
+    checkpoint_path_obj = Path(checkpoint_path)
+    if (checkpoint_path_obj / "config.json").exists():
+        model_path = str(checkpoint_path_obj)
+    elif (checkpoint_path_obj / "vqmodel_ema").exists():
+        model_path = str(checkpoint_path_obj / "vqmodel_ema")
+    elif (checkpoint_path_obj / "vqmodel").exists():
+        model_path = str(checkpoint_path_obj / "vqmodel")
+
+    if model_path is None:
+        model_path = HUGGINGFACE_REPO
+        print(f"Local checkpoint not found at '{checkpoint_path}', loading from HuggingFace: {HUGGINGFACE_REPO}")
+
+    # Skip redundant backbone weight download – from_pretrained will
+    # overwrite all parameters from the safetensors file anyway.
+    Sat3DGen._skip_backbone_weights = True
+    print(f"Loading model from {model_path} ...")
+    MODEL = Sat3DGen.from_pretrained(model_path).to(DEVICE)
+    Sat3DGen._skip_backbone_weights = False
+    MODEL.eval()
+    PATCH_SIZE = MODEL.unet_model.patch_size if hasattr(MODEL.unet_model, "patch_size") else 16
+    SAT_TRANSFORM = T.Compose([
+        T.Resize((PATCH_SIZE * 16, PATCH_SIZE * 16), interpolation=Image.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    print("Model loaded successfully.")
+
+
+# ---------------------------------------------------------------------------
+# Utility helpers (adapted from single_image_inference.py)
+# ---------------------------------------------------------------------------
+
+def save_obj(vertices: np.ndarray, faces: np.ndarray, colors: np.ndarray, filepath: str):
+    vertices = vertices @ np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]])
+    faces = faces[:, [2, 1, 0]]
+    mesh = o3d.geometry.TriangleMesh()
+    mesh.vertices = o3d.utility.Vector3dVector(vertices)
+    mesh.triangles = o3d.utility.Vector3iVector(faces)
+    mesh.vertex_colors = o3d.utility.Vector3dVector(colors / 255.0)
+    o3d.io.write_triangle_mesh(filepath, mesh, write_vertex_normals=False)
+
+
+def position_to_c2w(position: Tuple[float, float, float]) -> torch.Tensor:
+    rotation = compose_rotmat(0, 0, 0)
+    pos = np.array(position, dtype=np.float32)
+    pos[0] *= -1
+    pos = pos[[1, 0, 2]]
+    c2w = np.eye(4, dtype=np.float32)
+    c2w[:3, :3] = np.array(rotation, dtype=np.float32)
+    c2w[:3, 3] = pos
+    return torch.from_numpy(c2w).unsqueeze(0).to(DEVICE)
+
+
+def build_intrinsics() -> torch.Tensor:
+    fovx, fovy = 120, 120
+    fx = 0.5 * 256 / np.tan(0.5 * fovx / 180.0 * np.pi)
+    fy = 0.5 * 256 / np.tan(0.5 * fovy / 180.0 * np.pi)
+    cx = (256 - 1) / 2.0
+    cy = (256 - 1) / 2.0
+    intrinsics = np.array([[fx / 2, 0, cx / 2], [0, fy / 2, cy / 2], [0, 0, 1]], dtype=np.float32)
+    return torch.from_numpy(intrinsics).unsqueeze(0).to(DEVICE)
+
+
+def tensor_to_numpy_rgb(tensor: torch.Tensor) -> np.ndarray:
+    """Convert a [1, C, H, W] or [C, H, W] tensor in [0, 1] to a uint8 RGB numpy array."""
+    img = tensor.detach().cpu().clamp(0, 1)
+    if img.dim() == 4:
+        img = img.squeeze(0)
+    return (img.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
+
+
+def get_pano_rgb(output) -> torch.Tensor:
+    if hasattr(output.str_output, "sr_image"):
+        return output.str_output.sr_image
+    return output.str_output.image_raw_compo
+
+
+def get_per_rgb(output) -> torch.Tensor:
+    if hasattr(output.per_output, "sr_image"):
+        return output.per_output.sr_image
+    return output.per_output.image_raw_compo
+
+
+def make_histo(grd_img_path: str) -> torch.Tensor:
+    grd_img = Image.open(grd_img_path).convert("RGB").resize((512, 128))
+    grd_img = T.ToTensor()(grd_img).unsqueeze(0).float().to(DEVICE)
+
+    # Derive the sky-mask path by replacing only the parent directory name,
+    # keeping the filename intact (just switching extension to .png).
+    grd_path = Path(grd_img_path)
+    parent_name = grd_path.parent.name
+    if parent_name in ("streetview", "panorama"):
+        mask_dir = grd_path.parent.parent / "pano_sky_mask"
+        mask_img_path = str(mask_dir / grd_path.with_suffix(".png").name)
+    else:
+        raise ValueError(f"Cannot infer sky-mask path from {grd_img_path}")
+
+    mask_img = Image.open(mask_img_path).convert("L").resize((512, 128), Image.NEAREST)
+    mask_img = T.ToTensor()(mask_img).unsqueeze(0).float().to(DEVICE)
+
+    sky_image = (grd_img * mask_img).mul(2).sub(1)
+    sky_image = sky_image.detach().cpu().numpy()
+
+    from source.sky_histogram import compute_sky_histogram
+
+    histo_sky = torch.from_numpy(
+        compute_sky_histogram(sky_image[0], hist_range=(-1, 1))
+    ).unsqueeze(0).float().to(DEVICE)
+    return histo_sky
+
+
+def read_trajectory_from_csv(csv_path: str, sat_image_size: int) -> Tuple[List[Tuple[float, float, float]], np.ndarray]:
+    """Read a pre-generated trajectory .csv file (format: w,h,angle).
+
+    Returns:
+        positions: list of (x_norm, y_norm, z) in [-1, 1] range for rendering
+        pixel_coords: Nx2 array of pixel coordinates for visualization
+    """
+    half = sat_image_size / 2
+    positions = []
+    pixel_coords = []
+    with open(csv_path, "r") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            px = float(row["w"])
+            py = float(row["h"])
+            pixel_coords.append((px, py))
+            positions.append(((py - half) / half, (px - half) / half, -0.85))
+    return positions, np.array(pixel_coords, dtype=np.float32)
+
+
+def draw_trajectory_on_satellite(
+    sat_image_pil: Image.Image,
+    pixel_coords: np.ndarray,
+    active_index: Optional[int] = None,
+) -> np.ndarray:
+    """Draw trajectory on satellite image with glow effect (matching demo_inference style)."""
+    sat_frame = np.array(sat_image_pil.convert("RGB"))
+
+    if len(pixel_coords) >= 2:
+        # White outline pass (thicker, drawn first)
+        for idx in range(len(pixel_coords) - 1):
+            pt1 = tuple(np.round(pixel_coords[idx]).astype(int))
+            pt2 = tuple(np.round(pixel_coords[idx + 1]).astype(int))
+            cv2.line(sat_frame, pt1, pt2, (255, 255, 255), 3, cv2.LINE_AA)
+        # Colored line pass (thinner, on top)
+        for idx in range(len(pixel_coords) - 1):
+            pt1 = tuple(np.round(pixel_coords[idx]).astype(int))
+            pt2 = tuple(np.round(pixel_coords[idx + 1]).astype(int))
+            cv2.line(sat_frame, pt1, pt2, (255, 80, 80), 2, cv2.LINE_AA)
+
+    if active_index is not None and len(pixel_coords) > 0:
+        coord = pixel_coords[min(active_index, len(pixel_coords) - 1)]
+        px, py = int(round(coord[0])), int(round(coord[1]))
+        # Outer glow via alpha blending
+        overlay = sat_frame.copy()
+        cv2.circle(overlay, (px, py), 12, (0, 255, 100), -1, cv2.LINE_AA)
+        sat_frame = cv2.addWeighted(sat_frame, 0.7, overlay, 0.3, 0)
+        # Solid inner circle + white ring
+        cv2.circle(sat_frame, (px, py), 6, (0, 255, 100), -1, cv2.LINE_AA)
+        cv2.circle(sat_frame, (px, py), 7, (255, 255, 255), 2, cv2.LINE_AA)
+
+    return sat_frame
+
+
+def build_trajectory_preview(sat_image_pil: Image.Image, pixel_coords: np.ndarray) -> Image.Image:
+    sat_frame = draw_trajectory_on_satellite(sat_image_pil, pixel_coords)
+    preview = cv2.resize(
+        sat_frame,
+        (TRAJECTORY_PREVIEW_SIZE, TRAJECTORY_PREVIEW_SIZE),
+        interpolation=cv2.INTER_LINEAR,
+    )
+    return Image.fromarray(preview)
+
+
+def resolve_demo_sky_pairs(demo_dir: Path) -> Tuple[List[Tuple[Path, Path]], Optional[Path]]:
+    pano_dir = demo_dir / "panorama"
+    mask_dir = demo_dir / "pano_sky_mask"
+    if not pano_dir.exists() or not mask_dir.exists():
+        return [], None
+
+    mask_lookup = {mask_path.stem: mask_path for mask_path in sorted(mask_dir.glob("*.png"))}
+    sky_pairs: List[Tuple[Path, Path]] = []
+    for pano_path in sorted(pano_dir.glob("*")):
+        if pano_path.suffix.lower() not in {".jpg", ".jpeg", ".png"}:
+            continue
+        mask_path = mask_lookup.get(pano_path.stem)
+        if mask_path is not None:
+            sky_pairs.append((pano_path, mask_path))
+
+    if not sky_pairs:
+        return [], None
+
+    default_idx = 0
+    for idx, (pano_path, _) in enumerate(sky_pairs):
+        pano_name_lower = pano_path.name.lower()
+        pano_stem_lower = pano_path.stem.lower()
+        if pano_name_lower in DEFAULT_SKY_FILENAMES or "default" in pano_stem_lower:
+            default_idx = idx
+            break
+
+    ordered_pairs = [sky_pairs[default_idx], *sky_pairs[:default_idx], *sky_pairs[default_idx + 1 :]]
+    return ordered_pairs, ordered_pairs[0][0]
+
+
+# ---------------------------------------------------------------------------
+# Step 1: Satellite Image → 3D Mesh
+# ---------------------------------------------------------------------------
+
+def generate_mesh(sat_image_pil: Image.Image, mesh_resolution: int = 256, progress=gr.Progress()):
+    """Generate a 3D mesh from a satellite image."""
+    if sat_image_pil is None:
+        raise gr.Error("Please upload a satellite image first.")
+
+    print("[generate_mesh] >>> Start")
+    load_model()
+    print("[generate_mesh] Model loaded")
+
+    progress(0.1, desc="Preprocessing satellite image...")
+    print("[generate_mesh] Preprocessing satellite image...")
+    sat_input = SAT_TRANSFORM(sat_image_pil.convert("RGB")).unsqueeze(0).to(DEVICE)
+
+    progress(0.3, desc="Generating triplane features...")
+    print("[generate_mesh] Generating triplane features...")
+    with torch.no_grad():
+        triplane = MODEL.from_sat_to_triplane(sat_input)
+    print("[generate_mesh] Triplane generated successfully")
+
+    progress(0.5, desc="Extracting 3D mesh (this may take a moment)...")
+    print(f"[generate_mesh] Extracting 3D mesh (resolution={mesh_resolution})...")
+    with torch.no_grad():
+        vertices, faces, vertex_colors = MODEL.extract_mesh(triplane, mesh_resolution=mesh_resolution)
+    print(f"[generate_mesh] Mesh extracted: {vertices.shape[0]} vertices, {faces.shape[0]} faces")
+
+    vertices = vertices[:, [1, 2, 0]]
+
+    # Save mesh
+    mesh_path = str(RESULTS_DIR / "mesh.obj")
+    save_obj(vertices, faces, vertex_colors, mesh_path)
+    print(f"[generate_mesh] OBJ saved to {mesh_path}")
+
+    # Also save triplane to state for Step 2
+    state = {"triplane": triplane, "sat_image": sat_image_pil}
+
+    progress(0.9, desc="Preparing 3D visualization...")
+    print("[generate_mesh] Converting OBJ → GLB for 3D preview...")
+
+    # Create a glb file for Gradio's Model3D component.
+    # Use a tempfile so Gradio can reliably serve it via its file cache.
+    import tempfile, shutil
+    glb_path_local = str(RESULTS_DIR / "mesh.glb")
+    mesh_trimesh = trimesh.load(mesh_path, process=False)
+    # Ensure we have a single Trimesh (not a Scene) with vertex normals,
+    # otherwise Chrome's WebGL renderer shows a blank canvas.
+    if isinstance(mesh_trimesh, trimesh.Scene):
+        geometries = list(mesh_trimesh.geometry.values())
+        if geometries:
+            mesh_trimesh = trimesh.util.concatenate(geometries)
+        else:
+            raise gr.Error("Failed to load mesh geometry.")
+    if not hasattr(mesh_trimesh, 'vertex_normals') or mesh_trimesh.vertex_normals is None or len(mesh_trimesh.vertex_normals) == 0:
+        mesh_trimesh.vertex_normals  # triggers auto-computation
+    print(f"[generate_mesh] Mesh has {len(mesh_trimesh.vertices)} verts, {len(mesh_trimesh.faces)} faces, normals: {mesh_trimesh.vertex_normals.shape}")
+    mesh_trimesh.export(glb_path_local, file_type="glb")
+    print(f"[generate_mesh] GLB saved to {glb_path_local} ({os.path.getsize(glb_path_local)} bytes)")
+
+    tmp_glb = tempfile.NamedTemporaryFile(suffix=".glb", delete=False)
+    shutil.copy2(glb_path_local, tmp_glb.name)
+    tmp_glb.close()
+    print(f"[generate_mesh] GLB copied to temp file: {tmp_glb.name}")
+
+    progress(1.0, desc="Done!")
+    print("[generate_mesh] <<< 3D mesh generated successfully!")
+    return tmp_glb.name, mesh_path, state
+
+
+def download_mesh(mesh_path: str):
+    """Return the mesh file for download."""
+    if mesh_path and os.path.exists(mesh_path):
+        return mesh_path
+    return None
+
+
+# ---------------------------------------------------------------------------
+# Step 2: Trajectory → Panorama + Perspective Video
+# ---------------------------------------------------------------------------
+
+def render_trajectory_video(
+    sat_image_pil: Image.Image,
+    trajectory_csv_path: str,
+    sky_path: str,
+    progress=gr.Progress(),
+):
+    """Render panorama and perspective views along a pre-generated trajectory.
+
+    Layout per frame:
+      Top row:    satellite image (with camera marker)  |  panorama RGB
+      Bottom row: 4 perspective views in a horizontal row (left, front, right, back)
+    """
+    print("[render_trajectory_video] >>> Start")
+    load_model()
+
+    sat_size = sat_image_pil.size[0]
+    positions, pixel_coords = read_trajectory_from_csv(trajectory_csv_path, sat_size)
+    if len(positions) == 0:
+        raise gr.Error(f"Trajectory file is empty: {trajectory_csv_path}")
+    print(f"[render_trajectory_video] Loaded {len(positions)} positions from {trajectory_csv_path}")
+
+    progress(0.1, desc="Extracting triplane features...")
+    sat_tensor = SAT_TRANSFORM(sat_image_pil.convert("RGB")).unsqueeze(0).to(DEVICE)
+    with torch.no_grad():
+        triplane = MODEL.from_sat_to_triplane(sat_tensor)
+
+    progress(0.2, desc="Preparing sky condition...")
+    sky_hist = make_histo(sky_path)
+    with torch.no_grad():
+        w_sky = MODEL.w_sky_prepare(sky_hist)
+        sky_feature_2d = MODEL.w_sky2sky_feature_2D(w_sky, sky_hist)
+
+    progress(0.25, desc="Rendering views along trajectory...")
+    intrinsics = build_intrinsics()
+    yaw_values = [0, -90, 90, 180]
+
+    video_dir = RESULTS_DIR / "video_frames"
+    if video_dir.exists():
+        shutil.rmtree(video_dir)
+    video_dir.mkdir(parents=True, exist_ok=True)
+
+    total_positions = len(positions)
+    for idx, position in enumerate(positions):
+        progress(0.25 + 0.6 * idx / total_positions, desc=f"Rendering frame {idx + 1}/{total_positions}...")
+        if idx % 10 == 0 or idx == total_positions - 1:
+            print(f"[render_trajectory_video] Rendering frame {idx + 1}/{total_positions}...")
+
+        c2w = position_to_c2w(position)
+        c2w[:, :3, 3] = c2w[:, :3, 3] * MODEL.position_scale_factor
+
+        with torch.no_grad():
+            pano_result = MODEL.from_3D_to_results(
+                triplane,
+                c2w=c2w,
+                w_sky=w_sky,
+                sky_feature_2D=sky_feature_2d,
+                syn_pano=True,
+            )
+            pano_rgb = tensor_to_numpy_rgb(get_pano_rgb(pano_result))
+
+            per_views = []
+            for yaw in yaw_values:
+                c2w_per = c2w.clone()
+                c2w_per[:, :3, :3] = torch.from_numpy(compose_rotmat(0, 0, yaw)).unsqueeze(0).to(DEVICE)
+                per_result = MODEL.from_3D_to_results(
+                    triplane,
+                    c2w=c2w_per,
+                    w_sky=w_sky,
+                    intrinsics=intrinsics,
+                    sky_feature_2D=sky_feature_2d,
+                    syn_pano=False,
+                    syn_per=True,
+                )
+                per_rgb = tensor_to_numpy_rgb(get_per_rgb(per_result))
+                per_views.append(per_rgb)
+
+        # --- Satellite image with camera position marker ---
+        sat_frame = draw_trajectory_on_satellite(sat_image_pil, pixel_coords, active_index=idx)
+
+        # --- Compose frame ---
+        # Top row: satellite (square) | panorama RGB
+        pano_h, pano_w = pano_rgb.shape[:2]
+        sat_resized = cv2.resize(sat_frame, (pano_h, pano_h))
+        top_row = np.concatenate([sat_resized, pano_rgb], axis=1)
+
+        # Bottom row: 4 perspective views in a horizontal row (left, front, right, back)
+        # Flip back view for consistency
+        per_back = cv2.flip(per_views[3], 1)
+        per_row = np.concatenate([per_views[1], per_views[0], per_views[2], per_back], axis=1)
+
+        # Resize bottom row to match top row width
+        top_width = top_row.shape[1]
+        per_row_h = int(per_row.shape[0] * top_width / per_row.shape[1])
+        per_row_resized = cv2.resize(per_row, (top_width, per_row_h))
+
+        composed = np.concatenate([top_row, per_row_resized], axis=0)
+
+        frame_path = video_dir / f"{idx:04d}.png"
+        cv2.imwrite(str(frame_path), cv2.cvtColor(composed, cv2.COLOR_RGB2BGR))
+
+    progress(0.9, desc="Encoding video...")
+    print("[render_trajectory_video] All frames rendered, encoding video with ffmpeg...")
+    video_path = str(RESULTS_DIR / "trajectory_video.mp4")
+    ffmpeg_path = shutil.which("ffmpeg")
+    if ffmpeg_path is None:
+        raise gr.Error("ffmpeg not found. Please install ffmpeg to generate videos.")
+
+    subprocess.run([
+        ffmpeg_path, "-y", "-framerate", "5",
+        "-i", str(video_dir / "%04d.png"),
+        "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2",
+        "-c:v", "libx264", "-pix_fmt", "yuv420p",
+        video_path,
+    ], check=True, capture_output=True)
+
+    print(f"[render_trajectory_video] Video saved to {video_path}")
+    progress(1.0, desc="Done!")
+    return video_path
+
+
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+
+def build_demo():
+
+    # Find sample images from demo directory
+    demo_dir = Path(__file__).resolve().parent / "demo_images"
+    sample_sat_images = sorted((demo_dir / "satellite").glob("*.png")) if (demo_dir / "satellite").exists() else []
+    sample_sat_images_with_csv = [p for p in sample_sat_images if p.with_suffix(".csv").exists()]
+    sample_sky_pairs, default_sky_path = resolve_demo_sky_pairs(demo_dir)
+
+    with gr.Blocks(title="Sat3DGen Demo", theme=gr.themes.Soft()) as demo:
+        gr.Markdown(
+            """
+            ## Sat3DGen: 3D Scene Generation from Satellite Imagery
+
+            Upload a satellite image to **generate a 3D mesh** or **render a walkthrough video**.
+            """
+        )
+
+        # Shared state
+        inference_state = gr.State(value=None)
+        mesh_file_path = gr.State(value=None)
+
+        # ---- 3D Mesh Generation ----
+        with gr.Tab("3D Mesh Generation"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    sat_input = gr.Image(
+                        label="Upload Satellite Image",
+                        type="pil",
+                        height=400,
+                    )
+                    mesh_resolution_slider = gr.Slider(
+                        minimum=128, maximum=512, value=128, step=64,
+                        label="Mesh Resolution (voxel size)",
+                    )
+                    generate_button = gr.Button("🚀 Generate 3D Mesh", variant="primary", size="lg")
+
+                with gr.Column(scale=2):
+                    mesh_viewer = gr.Model3D(label="3D Mesh Preview", height=500)
+                    download_button = gr.DownloadButton("💾 Download Mesh (.obj)", variant="secondary")
+
+            if sample_sat_images:
+                gr.Markdown("### Sample Images")
+                gr.Examples(
+                    examples=[[str(p)] for p in sample_sat_images],
+                    inputs=[sat_input],
+                    label="Click to load a sample satellite image",
+                    examples_per_page=len(sample_sat_images),
+                )
+
+            gr.Markdown(
+                "⚠️ **Note:** The 3D mesh preview may show slight color distortion. "
+                "The cause is currently under investigation."
+            )
+
+            generate_button.click(
+                fn=generate_mesh,
+                inputs=[sat_input, mesh_resolution_slider],
+                outputs=[mesh_viewer, mesh_file_path, inference_state],
+            )
+            mesh_file_path.change(
+                fn=download_mesh,
+                inputs=[mesh_file_path],
+                outputs=[download_button],
+            )
+        # ---- Video Rendering ----
+        with gr.Tab("Video Rendering"):
+            # Hidden state to track the resolved trajectory .csv path
+            trajectory_csv_state = gr.State(value=None)
+            sky_path_state = gr.State(value=str(default_sky_path) if default_sky_path is not None else None)
+
+            def load_sat_from_gallery(evt: gr.SelectData):
+                """Load selected satellite image and check for a same-name trajectory .csv."""
+                if evt.index is None or evt.index >= len(sample_sat_images_with_csv):
+                    return None, None, "No image selected.", None
+                sat_path = sample_sat_images_with_csv[evt.index]
+                sat_pil = Image.open(str(sat_path))
+                csv_path = sat_path.with_suffix(".csv")
+                if csv_path.exists():
+                    status_msg = f"✅ Trajectory found: `{csv_path.name}`"
+                    _, pixel_coords = read_trajectory_from_csv(str(csv_path), sat_pil.size[0])
+                    preview = build_trajectory_preview(sat_pil, pixel_coords)
+                    return sat_pil, str(csv_path), status_msg, preview
+                status_msg = (
+                    f"⚠️ No trajectory file found. "
+                    f"Please pre-generate a trajectory and save it as "
+                    f"`{csv_path.name}` in `{sat_path.parent}/` using:\n\n"
+                    f"```\npython inference/make_trajectory.py "
+                    f"--input_img_path {sat_path} --save_same_name\n```"
+                )
+                return sat_pil, None, status_msg, None
+
+            def on_sat_upload(sat_image_pil):
+                """When user uploads a custom satellite image, no same-name trajectory CSV is available."""
+                if sat_image_pil is None:
+                    return None, "No image uploaded.", None
+                return None, (
+                    "⚠️ For uploaded images, you need a **trajectory .csv** file with the same name "
+                    "as your satellite image (e.g. `my_image.csv` for `my_image.png`).\n\n"
+                    "You can generate one interactively using either:\n\n"
+                    "- **Jupyter Notebook** (recommended): `inference/make_trajectory.ipynb`\n"
+                    "- **Command line**: "
+                    "`python inference/make_trajectory.py --input_img_path <your_image_path> --save_same_name`\n\n"
+                    "If you used the command line **without** `--save_same_name`, "
+                    "the CSV is saved under `results/<image_name>/pixels.csv`. "
+                    "You will need to **copy** it next to your satellite image with the same base name "
+                    "(e.g. copy to `demo_images/satellite/my_image.csv`)."
+                ), None
+
+            def load_sky_from_gallery(evt: gr.SelectData):
+                """Select one demo panorama street image. The first entry is the default."""
+                if not sample_sky_pairs:
+                    return None, None, "No demo panorama street image is available."
+                if evt.index is None or evt.index >= len(sample_sky_pairs):
+                    sky_path = default_sky_path
+                else:
+                    sky_path = sample_sky_pairs[evt.index][0]
+                default_suffix = " (Default)" if sky_path == default_sky_path else ""
+                status_msg = (
+                    f"Selected demo panorama: `{sky_path.name}`{default_suffix}\n\n"
+                    f"If you do not choose another one, this image will be used."
+                )
+                return str(sky_path), str(sky_path), status_msg
+
+            def render_video_from_state(sat_image, csv_path, sky_path, progress=gr.Progress()):
+                """Render video using the pre-generated trajectory CSV."""
+                if sat_image is None:
+                    raise gr.Error("Please select or upload a satellite image first.")
+                if csv_path is None or not Path(csv_path).exists():
+                    raise gr.Error(
+                        "No trajectory CSV found. Please pre-generate a trajectory using: "
+                        "python inference/make_trajectory.py --input_img_path <image> --save_same_name"
+                    )
+                resolved_sky_path = sky_path or (str(default_sky_path) if default_sky_path is not None else None)
+                if resolved_sky_path is None or not Path(resolved_sky_path).exists():
+                    raise gr.Error("No valid demo panorama is available. Please add one under demo_images/panorama.")
+                return render_trajectory_video(sat_image, csv_path, resolved_sky_path, progress)
+
+            # ===== Main layout =====
+            with gr.Row(equal_height=False):
+                # Left column: satellite image selection
+                with gr.Column(scale=1):
+                    sat_input_video = gr.Image(
+                        label="Upload Satellite Image",
+                        type="pil",
+                        height=300,
+                    )
+                    trajectory_status = gr.Markdown(value="Select a demo image or upload your own.")
+                    selected_sky_preview = gr.Image(
+                        label="Selected Demo Panorama",
+                        value=str(default_sky_path) if default_sky_path is not None else None,
+                        height=180,
+                    )
+                    default_sky_message = "No demo panorama street image is available."
+                    if default_sky_path is not None:
+                        default_sky_message = (
+                            f"Default demo panorama: `{default_sky_path.name}`\n\n"
+                            "If you do not select another demo panorama, this one will be used."
+                        )
+                    sky_status = gr.Markdown(value=default_sky_message)
+                    render_button = gr.Button("🎬 Render Video", variant="primary", size="lg")
+
+                # Middle column: trajectory preview
+                with gr.Column(scale=1):
+                    trajectory_preview = gr.Image(label="Trajectory Preview", height=300)
+
+                # Right column: video output
+                with gr.Column(scale=2):
+                    video_output = gr.Video(label="Rendered Video", height=500)
+
+            # ===== Sample Satellite Images Gallery (only those with a trajectory CSV) =====
+            if sample_sat_images_with_csv:
+                gr.Markdown("### 🛰️ Sample Satellite Images — click to load")
+                sat_gallery = gr.Gallery(
+                    value=[str(p) for p in sample_sat_images_with_csv],
+                    label="Sample Satellite Images (with trajectory)",
+                    columns=5,
+                    rows=1,
+                    height=120,
+                    object_fit="cover",
+                    allow_preview=False,
+                )
+                sat_gallery.select(
+                    fn=load_sat_from_gallery,
+                    inputs=None,
+                    outputs=[sat_input_video, trajectory_csv_state, trajectory_status, trajectory_preview],
+                )
+
+            if sample_sky_pairs:
+                gr.Markdown(
+                    "### 🌤️ Demo Panorama Street Images — the first one is the default\n\n"
+                    "The panorama image and its corresponding sky mask are used to extract a "
+                    "**sky region color histogram**, which serves as a **lighting condition hint** "
+                    "during street-view rendering. This only affects the appearance (illumination/color tone) "
+                    "of the rendered views — it does **not** alter the underlying 3D NeRF geometry."
+                )
+                sky_gallery = gr.Gallery(
+                    value=[
+                        (
+                            str(pano_path),
+                            f"{pano_path.name} (Default)" if pano_path == default_sky_path else pano_path.name,
+                        )
+                        for pano_path, _ in sample_sky_pairs
+                    ],
+                    label="Demo Panorama Street Images",
+                    columns=5,
+                    rows=1,
+                    height=120,
+                    object_fit="cover",
+                    allow_preview=False,
+                )
+                sky_gallery.select(
+                    fn=load_sky_from_gallery,
+                    inputs=None,
+                    outputs=[sky_path_state, selected_sky_preview, sky_status],
+                )
+
+            # When user uploads a custom image
+            sat_input_video.upload(
+                fn=on_sat_upload,
+                inputs=[sat_input_video],
+                outputs=[trajectory_csv_state, trajectory_status, trajectory_preview],
+            )
+
+            render_button.click(
+                fn=render_video_from_state,
+                inputs=[sat_input_video, trajectory_csv_state, sky_path_state],
+                outputs=[video_output],
+            )
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_demo()
+    port = int(os.environ.get("GRADIO_SERVER_PORT", 7860))
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=port,
+        share=False,
+        allowed_paths=[
+            str(Path(__file__).resolve().parent / "demo_images"),
+            str(Path(__file__).resolve().parent / "results"),
+        ],
+    )

demo_images/satellite/sat_demo_1.csv

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