Add images using LFS

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.ply filter=lfs diff=lfs merge=lfs -text
+*.PNG filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,238 @@
+import gradio as gr
+import os
+import struct
+import numpy as np
+
+# --- 1. Configuration ---
+SCENES = [
+    {
+        "name": "Office 0", 
+        "thumb": "assets/office0.PNG",           
+        "model": "assets/office0.ply",
+        "tracking_img": "assets/office0_tracking.PNG" 
+    },
+    {
+        "name": "Office 2",
+        "thumb": "assets/office2.PNG",
+        "model": "assets/office2.ply",
+        "tracking_img": "assets/office2_tracking.PNG"
+    },
+    {
+        "name": "Room 0",
+        "thumb": "assets/room0.PNG",
+        "model": "assets/room0.ply",
+        "tracking_img": "assets/room0_tracking.PNG"
+    }
+]
+
+gallery_items = [(item["thumb"], item["name"]) for item in SCENES]
+
+# --- 2. 讀取並轉換 Gaussian Splatting PLY ---
+def read_gaussian_ply(filepath):
+    """讀取 Gaussian Splatting PLY 並提取位置和顏色"""
+    
+    with open(filepath, 'rb') as f:
+        # 讀取 header
+        header = []
+        while True:
+            line = f.readline().decode('utf-8').strip()
+            header.append(line)
+            if line == 'end_header':
+                break
+        
+        # 解析 vertex 數量
+        vertex_count = 0
+        for line in header:
+            if line.startswith('element vertex'):
+                vertex_count = int(line.split()[-1])
+                break
+        
+        print(f"Reading {vertex_count:,} Gaussian splats...")
+        
+        # 讀取二進制數據
+        # 每個 vertex: 17 個 float (x,y,z, nx,ny,nz, f_dc_0,f_dc_1,f_dc_2, opacity, scale*3, rot*4)
+        bytes_per_vertex = 17 * 4  # 17 floats * 4 bytes
+        data = f.read(vertex_count * bytes_per_vertex)
+        
+        # 解析數據
+        vertices = []
+        colors = []
+        
+        for i in range(vertex_count):
+            offset = i * bytes_per_vertex
+            vertex_data = struct.unpack('17f', data[offset:offset + bytes_per_vertex])
+            
+            # 提取位置 (x, y, z)
+            x, y, z = vertex_data[0], vertex_data[1], vertex_data[2]
+            vertices.append([x, y, z])
+            
+            # 提取顏色 (f_dc_0, f_dc_1, f_dc_2) 並轉換到 [0, 255]
+            # Gaussian Splatting 使用球諧函數,f_dc 是 DC 分量
+            # 轉換公式: color = (0.5 + SH_C0 * f_dc) * 255
+            SH_C0 = 0.28209479177387814
+            r = max(0, min(255, int((0.5 + SH_C0 * vertex_data[6]) * 255)))
+            g = max(0, min(255, int((0.5 + SH_C0 * vertex_data[7]) * 255)))
+            b = max(0, min(255, int((0.5 + SH_C0 * vertex_data[8]) * 255)))
+            colors.append([r, g, b])
+        
+        return np.array(vertices, dtype=np.float32), np.array(colors, dtype=np.uint8)
+
+# --- 3. 轉換為標準 PLY ---
+def convert_to_standard_ply(input_path, output_path):
+    """將 Gaussian Splatting PLY 轉換為標準帶顏色的 PLY"""
+    
+    vertices, colors = read_gaussian_ply(input_path)
+    
+    # 寫入 ASCII PLY
+    with open(output_path, 'w') as f:
+        f.write('ply\n')
+        f.write('format ascii 1.0\n')
+        f.write(f'element vertex {len(vertices)}\n')
+        f.write('property float x\n')
+        f.write('property float y\n')
+        f.write('property float z\n')
+        f.write('property uchar red\n')
+        f.write('property uchar green\n')
+        f.write('property uchar blue\n')
+        f.write('end_header\n')
+        
+        for vertex, color in zip(vertices, colors):
+            f.write(f'{vertex[0]} {vertex[1]} {vertex[2]} {color[0]} {color[1]} {color[2]}\n')
+    
+    print(f"✅ Converted to: {output_path}")
+
+# --- 4. 初始化 - 轉換所有文件 ---
+def initialize_converted_models():
+    """轉換所有 Gaussian Splatting PLY 為標準格式"""
+    
+    converted_dir = "converted"
+    os.makedirs(converted_dir, exist_ok=True)
+    
+    converted_scenes = []
+    
+    for scene in SCENES:
+        input_ply = scene["model"]
+        output_ply = f"{converted_dir}/{os.path.basename(input_ply)}"
+        
+        if not os.path.exists(output_ply):
+            print(f"\n🔄 Converting {scene['name']}...")
+            try:
+                convert_to_standard_ply(input_ply, output_ply)
+            except Exception as e:
+                print(f"❌ Error converting {input_ply}: {e}")
+                continue
+        else:
+            print(f"✅ Already converted: {output_ply}")
+        
+        # 更新場景配置
+        converted_scenes.append({
+            **scene,
+            "converted_model": output_ply
+        })
+    
+    return converted_scenes
+
+# --- 5. 選擇處理 ---
+def create_select_handler(converted_scenes):
+    """創建選擇處理函數"""
+    def on_select(evt: gr.SelectData):
+        index = evt.index
+        scene = converted_scenes[index]
+        
+        if "converted_model" not in scene or not os.path.exists(scene["converted_model"]):
+            return (
+                None,
+                scene["tracking_img"],
+                f"**Error: Converted model not found for {scene['name']}**"
+            )
+        
+        file_size = os.path.getsize(scene["converted_model"]) / (1024 * 1024)
+        
+        print(f"\n📂 Loading: {scene['name']}")
+        print(f"   File: {scene['converted_model']}")
+        print(f"   Size: {file_size:.1f} MB\n")
+        
+        return (
+            scene["converted_model"],
+            scene["tracking_img"],
+            f"**{scene['name']}** ({file_size:.1f} MB) ✓"
+        )
+    
+    return on_select
+
+# --- 6. UI ---
+print("\n" + "="*60)
+print("🚀 RGS-SLAM Viewer - Initializing...")
+print("="*60)
+
+# 轉換模型
+converted_scenes = initialize_converted_models()
+
+print("\n" + "="*60)
+print("✅ Ready to launch!")
+print("="*60 + "\n")
+
+with gr.Blocks(title="RGS-SLAM Demo") as demo:
+    
+    gr.Markdown("# 🚀 RGS-SLAM Gaussian Splatting Viewer")
+    gr.Markdown(
+        "**3D Gaussian Splatting** reconstruction results with camera trajectories. "
+        "Models are automatically converted to viewable format."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📂 Select Scene")
+            scene_gallery = gr.Gallery(
+                value=gallery_items, 
+                label="Available Scenes",
+                columns=1,
+                height=400,
+                object_fit="contain"
+            )
+            
+            gr.Markdown("""
+            ### 🎮 Controls
+            - **Rotate**: Left mouse drag
+            - **Zoom**: Mouse wheel
+            - **Pan**: Right mouse drag
+            """)
+        
+        with gr.Column(scale=3):
+            status = gr.Markdown("**👈 Select a scene to begin**")
+            
+            with gr.Tab("🎯 3D Reconstruction"):
+                gr.Markdown("### Point Cloud with Colors")
+                model_viewer = gr.Model3D(
+                    label="Gaussian Splat Visualization",
+                    clear_color=[0.1, 0.1, 0.1, 1.0],
+                    height=600
+                )
+                gr.Markdown("*Colors extracted from Gaussian Splatting's spherical harmonics (f_dc)*")
+            
+            with gr.Tab("📍 Camera Trajectory"):
+                gr.Markdown("### SLAM Tracking Results")
+                trajectory_img = gr.Image(
+                    label="Camera Path",
+                    height=600
+                )
+    
+    # Event binding
+    select_handler = create_select_handler(converted_scenes)
+    
+    scene_gallery.select(
+        fn=select_handler,
+        outputs=[model_viewer, trajectory_img, status]
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown(
+        "**Note:** Gaussian Splatting PLY files contain spherical harmonics coefficients. "
+        "Colors are extracted from the DC component (f_dc_0, f_dc_1, f_dc_2) for visualization."
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860
+    )

assets/office0.ply

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e4993a8a3ff0e4aed46b765feefde8cd64e4578d3e818cdc5451b8491e070cb
+size 16192712

assets/office2.ply

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71bfcaaac3bff99ad34a435877be0c4ce87bf6c75728ee457f19b9ea74333059
+size 26793844

assets/room0.ply

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1ebdf314fb5d4f0bbc0ad3b58b605c1a89432347a99a61fad2285976cee09fc
+size 19860972

requirements.txt

@@ -0,0 +1,5 @@
+gradio>=4.0.0
+trimesh
+plotly
+numpy
+scipy