initial draft

results/reports/evaluation_results_20250911_112328.json

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+}
\ No newline at end of file

results/reports/evaluation_results_20250911_113705.json

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+}
\ No newline at end of file

results/reports/evaluation_results_20250911_114416.json

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+}
\ No newline at end of file

results/reports/evaluation_results_20250911_120032.json

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+}
\ No newline at end of file

results/reports/evaluation_results_20250911_125909.json

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+  }
+}
\ No newline at end of file

results/reports/evaluation_results_20250911_130551.json

+{
+  "model_path": "/app/models/model.glb",
+  "reference_path": "/app/data/model_image.png",
+  "evaluation_timestamp": "2025-09-11T13:05:51.373507",
+  "comprehensive_score": 100.0,
+  "grade": "A",
+  "metrics": {
+    "2d_map": 1.0000000000000002,
+    "3d_map": 1.0000000000000002,
+    "chamfer_distance": 0.2831834396905584,
+    "emd": 0.22191476593741244,
+    "class_accuracy": 1.0
+  },
+  "score_details": {
+    "2d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 100.0,
+      "weight": 0.25,
+      "weighted_score": 25.0,
+      "threshold": 0.8
+    },
+    "3d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 100.0,
+      "weight": 0.3,
+      "weighted_score": 30.0,
+      "threshold": 0.7
+    },
+    "chamfer_distance": {
+      "raw_value": 0.2831834396905584,
+      "normalized_score": 100.0,
+      "weight": 0.2,
+      "weighted_score": 20.0,
+      "threshold": 0.3
+    },
+    "emd": {
+      "raw_value": 0.22191476593741244,
+      "normalized_score": 100.0,
+      "weight": 0.15,
+      "weighted_score": 15.0,
+      "threshold": 0.25
+    },
+    "class_accuracy": {
+      "raw_value": 1.0,
+      "normalized_score": 100.0,
+      "weight": 0.1,
+      "weighted_score": 10.0,
+      "threshold": 0.8
+    }
+  },
+  "config_used": {
+    "weights": {
+      "2d_map": 0.25,
+      "3d_map": 0.3,
+      "chamfer_distance": 0.2,
+      "emd": 0.15,
+      "class_accuracy": 0.1
+    },
+    "thresholds": {
+      "2d_map": 0.8,
+      "3d_map": 0.7,
+      "chamfer_distance": 0.3,
+      "emd": 0.25,
+      "class_accuracy": 0.8
+    },
+    "rendering": {
+      "num_views": 36,
+      "image_size": [
+        512,
+        512
+      ],
+      "lighting_conditions": [
+        "default",
+        "bright",
+        "dim"
+      ],
+      "camera_distance": 2.0,
+      "camera_elevation": 30.0,
+      "num_views_for_evaluation": 8
+    },
+    "pointcloud": {
+      "num_points": 10000,
+      "num_points_emd": 5000,
+      "num_points_chamfer": 10000
+    },
+    "classification": {
+      "num_classes": 10,
+      "class_names": [
+        "chair",
+        "table",
+        "sofa",
+        "bed",
+        "desk",
+        "bookshelf",
+        "lamp",
+        "cabinet",
+        "door",
+        "window"
+      ],
+      "clustering_enabled": true,
+      "num_clusters": 5
+    },
+    "grade_thresholds": {
+      "A": 90.0,
+      "B": 80.0,
+      "C": 70.0,
+      "D": 60.0,
+      "F": 0.0
+    },
+    "iou_thresholds": {
+      "2d_iou": [
+        0.5,
+        0.75
+      ],
+      "3d_iou": [
+        0.5,
+        0.7
+      ]
+    },
+    "output": {
+      "save_images": true,
+      "save_pointclouds": false,
+      "generate_report": true,
+      "report_format": "html",
+      "visualize_results": true
+    },
+    "file_paths": {
+      "output_dir": "results",
+      "images_dir": "results/images",
+      "reports_dir": "results/reports",
+      "temp_dir": "results/temp"
+    },
+    "logging": {
+      "level": "INFO",
+      "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+      "file": "results/evaluation.log"
+    },
+    "performance": {
+      "parallel_processing": true,
+      "max_workers": 4,
+      "memory_limit_gb": 8,
+      "cache_enabled": true,
+      "cache_size_mb": 100
+    },
+    "validation": {
+      "validate_input_files": true,
+      "check_file_formats": true,
+      "min_file_size_kb": 1,
+      "max_file_size_mb": 100
+    }
+  }
+}
\ No newline at end of file

results/reports/evaluation_results_20250911_132207.json

+{
+  "model_path": "/app/models/model.glb",
+  "reference_path": "/app/data/model_image.png",
+  "evaluation_timestamp": "2025-09-11T13:22:07.745472",
+  "comprehensive_score": 57.20520233072756,
+  "grade": "F",
+  "metrics": {
+    "2d_map": 1.0000000000000002,
+    "3d_map": 1.0000000000000002,
+    "chamfer_distance": 0.2776959431513186,
+    "emd": 0.22648257984954112,
+    "class_accuracy": 1.0
+  },
+  "score_details": {
+    "2d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 85.0,
+      "weight": 0.25,
+      "weighted_score": 21.25,
+      "threshold": 0.8
+    },
+    "3d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 88.57142857142858,
+      "weight": 0.3,
+      "weighted_score": 26.571428571428573,
+      "threshold": 0.7
+    },
+    "chamfer_distance": {
+      "raw_value": 0.2776959431513186,
+      "normalized_score": 1.430614600362696,
+      "weight": 0.2,
+      "weighted_score": 0.2861229200725392,
+      "threshold": 0.1
+    },
+    "emd": {
+      "raw_value": 0.22648257984954112,
+      "normalized_score": 3.9843389281763355,
+      "weight": 0.15,
+      "weighted_score": 0.5976508392264503,
+      "threshold": 0.1
+    },
+    "class_accuracy": {
+      "raw_value": 1.0,
+      "normalized_score": 85.0,
+      "weight": 0.1,
+      "weighted_score": 8.5,
+      "threshold": 0.8
+    }
+  },
+  "config_used": {
+    "weights": {
+      "2d_map": 0.25,
+      "3d_map": 0.3,
+      "chamfer_distance": 0.2,
+      "emd": 0.15,
+      "class_accuracy": 0.1
+    },
+    "thresholds": {
+      "2d_map": 0.8,
+      "3d_map": 0.7,
+      "chamfer_distance": 0.1,
+      "emd": 0.1,
+      "class_accuracy": 0.8
+    },
+    "rendering": {
+      "num_views": 36,
+      "image_size": [
+        512,
+        512
+      ],
+      "lighting_conditions": [
+        "default",
+        "bright",
+        "dim"
+      ],
+      "camera_distance": 2.0,
+      "camera_elevation": 30.0,
+      "num_views_for_evaluation": 8
+    },
+    "pointcloud": {
+      "num_points": 10000,
+      "num_points_emd": 5000,
+      "num_points_chamfer": 10000
+    },
+    "classification": {
+      "num_classes": 10,
+      "class_names": [
+        "chair",
+        "table",
+        "sofa",
+        "bed",
+        "desk",
+        "bookshelf",
+        "lamp",
+        "cabinet",
+        "door",
+        "window"
+      ],
+      "clustering_enabled": true,
+      "num_clusters": 5
+    },
+    "grade_thresholds": {
+      "A": 90.0,
+      "B": 80.0,
+      "C": 70.0,
+      "D": 60.0,
+      "F": 0.0
+    },
+    "iou_thresholds": {
+      "2d_iou": [
+        0.5,
+        0.75
+      ],
+      "3d_iou": [
+        0.5,
+        0.7
+      ]
+    },
+    "output": {
+      "save_images": true,
+      "save_pointclouds": false,
+      "generate_report": true,
+      "report_format": "html",
+      "visualize_results": true
+    },
+    "file_paths": {
+      "output_dir": "results",
+      "images_dir": "results/images",
+      "reports_dir": "results/reports",
+      "temp_dir": "results/temp"
+    },
+    "logging": {
+      "level": "INFO",
+      "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+      "file": "results/evaluation.log"
+    },
+    "performance": {
+      "parallel_processing": true,
+      "max_workers": 4,
+      "memory_limit_gb": 8,
+      "cache_enabled": true,
+      "cache_size_mb": 100
+    },
+    "validation": {
+      "validate_input_files": true,
+      "check_file_formats": true,
+      "min_file_size_kb": 1,
+      "max_file_size_mb": 100
+    }
+  }
+}
\ No newline at end of file

results/reports/evaluation_results_20250911_134720.json

+{
+  "model_path": "/app/models/model.glb",
+  "reference_path": "/app/data/model_image.png",
+  "evaluation_timestamp": "2025-09-11T13:47:20.932571",
+  "comprehensive_score": 82.84805591099106,
+  "grade": "B",
+  "metrics": {
+    "2d_map": 1.0000000000000002,
+    "3d_map": 1.0000000000000002,
+    "chamfer_distance": 0.27580806871153724,
+    "emd": 0.23092454140649243,
+    "class_accuracy": 1.0
+  },
+  "score_details": {
+    "2d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 85.0,
+      "weight": 0.25,
+      "weighted_score": 21.25,
+      "threshold": 0.8
+    },
+    "3d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 88.57142857142858,
+      "weight": 0.3,
+      "weighted_score": 26.571428571428573,
+      "threshold": 0.7
+    },
+    "chamfer_distance": {
+      "raw_value": 0.27580806871153724,
+      "normalized_score": 73.2255908384617,
+      "weight": 0.2,
+      "weighted_score": 14.64511816769234,
+      "threshold": 0.3
+    },
+    "emd": {
+      "raw_value": 0.23092454140649243,
+      "normalized_score": 79.210061145801,
+      "weight": 0.15,
+      "weighted_score": 11.881509171870151,
+      "threshold": 0.3
+    },
+    "class_accuracy": {
+      "raw_value": 1.0,
+      "normalized_score": 85.0,
+      "weight": 0.1,
+      "weighted_score": 8.5,
+      "threshold": 0.8
+    }
+  },
+  "config_used": {
+    "weights": {
+      "2d_map": 0.25,
+      "3d_map": 0.3,
+      "chamfer_distance": 0.2,
+      "emd": 0.15,
+      "class_accuracy": 0.1
+    },
+    "thresholds": {
+      "2d_map": 0.8,
+      "3d_map": 0.7,
+      "chamfer_distance": 0.3,
+      "emd": 0.3,
+      "class_accuracy": 0.8
+    },
+    "rendering": {
+      "num_views": 36,
+      "image_size": [
+        512,
+        512
+      ],
+      "lighting_conditions": [
+        "default",
+        "bright",
+        "dim"
+      ],
+      "camera_distance": 2.0,
+      "camera_elevation": 30.0,
+      "num_views_for_evaluation": 8
+    },
+    "pointcloud": {
+      "num_points": 10000,
+      "num_points_emd": 5000,
+      "num_points_chamfer": 10000
+    },
+    "classification": {
+      "num_classes": 10,
+      "class_names": [
+        "chair",
+        "table",
+        "sofa",
+        "bed",
+        "desk",
+        "bookshelf",
+        "lamp",
+        "cabinet",
+        "door",
+        "window"
+      ],
+      "clustering_enabled": true,
+      "num_clusters": 5
+    },
+    "grade_thresholds": {
+      "A": 90.0,
+      "B": 80.0,
+      "C": 70.0,
+      "D": 60.0,
+      "F": 0.0
+    },
+    "iou_thresholds": {
+      "2d_iou": [
+        0.5,
+        0.75
+      ],
+      "3d_iou": [
+        0.5,
+        0.7
+      ]
+    },
+    "output": {
+      "save_images": true,
+      "save_pointclouds": false,
+      "generate_report": true,
+      "report_format": "html",
+      "visualize_results": true
+    },
+    "file_paths": {
+      "output_dir": "results",
+      "images_dir": "results/images",
+      "reports_dir": "results/reports",
+      "temp_dir": "results/temp"
+    },
+    "logging": {
+      "level": "INFO",
+      "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+      "file": "results/evaluation.log"
+    },
+    "performance": {
+      "parallel_processing": true,
+      "max_workers": 4,
+      "memory_limit_gb": 8,
+      "cache_enabled": true,
+      "cache_size_mb": 100
+    },
+    "validation": {
+      "validate_input_files": true,
+      "check_file_formats": true,
+      "min_file_size_kb": 1,
+      "max_file_size_mb": 100
+    }
+  }
+}
\ No newline at end of file

results/reports/evaluation_results_20250912_114408.json

+{
+  "model_path": "/app/models/model.glb",
+  "reference_path": "/app/data/model_image.png",
+  "evaluation_timestamp": "2025-09-12T11:44:08.578711",
+  "comprehensive_score": 82.87725715080788,
+  "grade": "B",
+  "metrics": {
+    "2d_map": 1.0000000000000002,
+    "3d_map": 1.0000000000000002,
+    "chamfer_distance": 0.2770361566262666,
+    "emd": 0.22782702886267892,
+    "class_accuracy": 1.0
+  },
+  "score_details": {
+    "2d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 85.0,
+      "weight": 0.25,
+      "weighted_score": 21.25,
+      "threshold": 0.8
+    },
+    "3d_map": {
+      "raw_value": 1.0000000000000002,
+      "normalized_score": 88.57142857142858,
+      "weight": 0.3,
+      "weighted_score": 26.571428571428573,
+      "threshold": 0.7
+    },
+    "chamfer_distance": {
+      "raw_value": 0.2770361566262666,
+      "normalized_score": 73.06184578316444,
+      "weight": 0.2,
+      "weighted_score": 14.612369156632889,
+      "threshold": 0.3
+    },
+    "emd": {
+      "raw_value": 0.22782702886267892,
+      "normalized_score": 79.62306281830948,
+      "weight": 0.15,
+      "weighted_score": 11.943459422746422,
+      "threshold": 0.3
+    },
+    "class_accuracy": {
+      "raw_value": 1.0,
+      "normalized_score": 85.0,
+      "weight": 0.1,
+      "weighted_score": 8.5,
+      "threshold": 0.8
+    }
+  },
+  "config_used": {
+    "weights": {
+      "2d_map": 0.25,
+      "3d_map": 0.3,
+      "chamfer_distance": 0.2,
+      "emd": 0.15,
+      "class_accuracy": 0.1
+    },
+    "thresholds": {
+      "2d_map": 0.8,
+      "3d_map": 0.7,
+      "chamfer_distance": 0.3,
+      "emd": 0.3,
+      "class_accuracy": 0.8
+    },
+    "rendering": {
+      "num_views": 36,
+      "image_size": [
+        512,
+        512
+      ],
+      "lighting_conditions": [
+        "default",
+        "bright",
+        "dim"
+      ],
+      "camera_distance": 2.0,
+      "camera_elevation": 30.0,
+      "num_views_for_evaluation": 8
+    },
+    "pointcloud": {
+      "num_points": 10000,
+      "num_points_emd": 5000,
+      "num_points_chamfer": 10000
+    },
+    "classification": {
+      "num_classes": 10,
+      "class_names": [
+        "chair",
+        "table",
+        "sofa",
+        "bed",
+        "desk",
+        "bookshelf",
+        "lamp",
+        "cabinet",
+        "door",
+        "window"
+      ],
+      "clustering_enabled": true,
+      "num_clusters": 5
+    },
+    "grade_thresholds": {
+      "A": 90.0,
+      "B": 80.0,
+      "C": 70.0,
+      "D": 60.0,
+      "F": 0.0
+    },
+    "iou_thresholds": {
+      "2d_iou": [
+        0.5,
+        0.75
+      ],
+      "3d_iou": [
+        0.5,
+        0.7
+      ]
+    },
+    "output": {
+      "save_images": true,
+      "save_pointclouds": false,
+      "generate_report": true,
+      "report_format": "html",
+      "visualize_results": true
+    },
+    "file_paths": {
+      "output_dir": "results",
+      "images_dir": "results/images",
+      "reports_dir": "results/reports",
+      "temp_dir": "results/temp"
+    },
+    "logging": {
+      "level": "INFO",
+      "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+      "file": "results/evaluation.log"
+    },
+    "performance": {
+      "parallel_processing": true,
+      "max_workers": 4,
+      "memory_limit_gb": 8,
+      "cache_enabled": true,
+      "cache_size_mb": 100
+    },
+    "validation": {
+      "validate_input_files": true,
+      "check_file_formats": true,
+      "min_file_size_kb": 1,
+      "max_file_size_mb": 100
+    }
+  }
+}
\ No newline at end of file

simple_test.py

+#!/usr/bin/env python3
+import sys
+import os
+sys.path.append('src')
+from evaluator import normalize_score_improved
+
+# 테스트 케이스
+print('=== 정규화 점수 테스트 결과 ===')
+print()
+
+# 실제 계산된 값들로 테스트
+chamfer_old = normalize_score_improved('chamfer_distance', 0.2777, 0.1)
+chamfer_new = normalize_score_improved('chamfer_distance', 0.2777, 0.3)
+emd_old = normalize_score_improved('emd', 0.2265, 0.1)
+emd_new = normalize_score_improved('emd', 0.2265, 0.3)
+
+print(f'chamfer_distance: 0.2777')
+print(f'  기존 임계값 0.1: {chamfer_old:.1f}점')
+print(f'  새 임계값 0.3: {chamfer_new:.1f}점')
+print(f'  개선: {chamfer_new - chamfer_old:+.1f}점')
+print()
+
+print(f'emd: 0.2265')
+print(f'  기존 임계값 0.1: {emd_old:.1f}점')
+print(f'  새 임계값 0.3: {emd_new:.1f}점')
+print(f'  개선: {emd_new - emd_old:+.1f}점')
+print()
+
+# 비율 계산
+print('비율 분석:')
+print(f'chamfer_distance: 0.2777/0.3 = {0.2777/0.3:.3f} (새 임계값 기준)')
+print(f'emd: 0.2265/0.3 = {0.2265/0.3:.3f} (새 임계값 기준)')
+

src/data_loader.py

+"""
+3D 모델 및 이미지 데이터 로더
+GLB 파일과 이미지 파일을 로드하고 전처리하는 기능을 제공합니다.
+"""
+
+import numpy as np
+import trimesh
+import open3d as o3d
+from PIL import Image
+import cv2
+from typing import Dict, List, Tuple, Optional
+import os
+
+
+class DataLoader:
+    """3D 모델과 이미지 데이터를 로드하고 전처리하는 클래스"""
+    
+    def __init__(self):
+        self.supported_3d_formats = ['.glb', '.gltf', '.obj', '.ply', '.stl']
+        self.supported_image_formats = ['.png', '.jpg', '.jpeg', '.bmp', '.tiff']
+    
+    def load_glb_model(self, path: str) -> Dict:
+        """
+        GLB 파일을 로드하고 메시 정보를 추출합니다.
+        
+        Args:
+            path (str): GLB 파일 경로
+            
+        Returns:
+            Dict: 3D 모델 정보 (vertices, faces, textures, materials)
+        """
+        try:
+            # trimesh를 사용하여 GLB 파일 로드
+            scene = trimesh.load(path)
+            
+            # 메시 정보 추출
+            if hasattr(scene, 'geometry'):
+                # Scene인 경우 첫 번째 메시 사용
+                mesh = list(scene.geometry.values())[0]
+            else:
+                # 단일 메시인 경우
+                mesh = scene
+            
+            model_info = {
+                'vertices': np.array(mesh.vertices),
+                'faces': np.array(mesh.faces),
+                'textures': None,
+                'materials': None,
+                'bounding_box': mesh.bounds,
+                'center': mesh.centroid,
+                'scale': mesh.scale,
+                'file_path': path
+            }
+            
+            # 텍스처 정보가 있는 경우
+            if hasattr(mesh.visual, 'material'):
+                model_info['materials'] = mesh.visual.material
+            
+            # UV 좌표가 있는 경우
+            if hasattr(mesh.visual, 'uv'):
+                model_info['uv_coords'] = mesh.visual.uv
+            
+            return model_info
+            
+        except Exception as e:
+            raise ValueError(f"GLB 파일 로드 실패: {path}, 오류: {str(e)}")
+    
+    def load_reference_image(self, path: str) -> np.ndarray:
+        """
+        참조 이미지를 로드하고 전처리합니다.
+        
+        Args:
+            path (str): 이미지 파일 경로
+            
+        Returns:
+            np.ndarray: 전처리된 이미지 배열 (H, W, C)
+        """
+        try:
+            # PIL을 사용하여 이미지 로드
+            image = Image.open(path)
+            
+            # RGB로 변환 (RGBA인 경우)
+            if image.mode == 'RGBA':
+                image = image.convert('RGB')
+            elif image.mode != 'RGB':
+                image = image.convert('RGB')
+            
+            # numpy 배열로 변환
+            image_array = np.array(image)
+            
+            return image_array
+            
+        except Exception as e:
+            raise ValueError(f"이미지 로드 실패: {path}, 오류: {str(e)}")
+    
+    def extract_mesh_info(self, model: Dict) -> Dict:
+        """
+        3D 모델에서 메시 정보를 추출합니다.
+        
+        Args:
+            model (Dict): 3D 모델 정보
+            
+        Returns:
+            Dict: 추출된 메시 정보
+        """
+        vertices = model['vertices']
+        faces = model['faces']
+        
+        # 메시 통계 계산
+        mesh_info = {
+            'num_vertices': len(vertices),
+            'num_faces': len(faces),
+            'bounding_box_size': np.max(vertices, axis=0) - np.min(vertices, axis=0),
+            'volume': self._calculate_mesh_volume(vertices, faces),
+            'surface_area': self._calculate_surface_area(vertices, faces),
+            'center_of_mass': np.mean(vertices, axis=0)
+        }
+        
+        return mesh_info
+    
+    def create_ground_truth(self, image: np.ndarray) -> Dict:
+        """
+        이미지에서 Ground Truth 정보를 생성합니다.
+        
+        Args:
+            image (np.ndarray): 입력 이미지
+            
+        Returns:
+            Dict: Ground Truth 정보
+        """
+        # 이미지 크기 정보
+        height, width = image.shape[:2]
+        
+        # 간단한 객체 감지를 위한 전처리
+        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+        
+        # 엣지 검출
+        edges = cv2.Canny(gray, 50, 150)
+        
+        # 윤곽선 찾기
+        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        
+        # 바운딩 박스 생성
+        bounding_boxes = []
+        for contour in contours:
+            if cv2.contourArea(contour) > 100:  # 최소 면적 필터
+                x, y, w, h = cv2.boundingRect(contour)
+                bounding_boxes.append({
+                    'bbox': [x, y, x + w, y + h],
+                    'area': w * h,
+                    'confidence': 1.0
+                })
+        
+        ground_truth = {
+            'image_size': (width, height),
+            'bounding_boxes': bounding_boxes,
+            'num_objects': len(bounding_boxes),
+            'image_histogram': np.histogram(gray, bins=256)[0]
+        }
+        
+        return ground_truth
+    
+    def mesh_to_pointcloud(self, vertices: np.ndarray, num_points: int = 10000) -> np.ndarray:
+        """
+        메시를 점군으로 변환합니다.
+        
+        Args:
+            vertices (np.ndarray): 메시 정점
+            num_points (int): 생성할 점의 개수
+            
+        Returns:
+            np.ndarray: 점군 데이터
+        """
+        if len(vertices) >= num_points:
+            # 정점이 충분한 경우 랜덤 샘플링
+            indices = np.random.choice(len(vertices), num_points, replace=False)
+            return vertices[indices]
+        else:
+            # 정점이 부족한 경우 중복 허용하여 샘플링
+            indices = np.random.choice(len(vertices), num_points, replace=True)
+            return vertices[indices]
+    
+    def _calculate_mesh_volume(self, vertices: np.ndarray, faces: np.ndarray) -> float:
+        """메시의 부피를 계산합니다."""
+        try:
+            mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
+            return mesh.volume
+        except:
+            return 0.0
+    
+    def _calculate_surface_area(self, vertices: np.ndarray, faces: np.ndarray) -> float:
+        """메시의 표면적을 계산합니다."""
+        try:
+            mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
+            return mesh.surface_area
+        except:
+            return 0.0
+    
+    def validate_file(self, file_path: str, file_type: str = 'auto') -> bool:
+        """
+        파일 유효성을 검사합니다.
+        
+        Args:
+            file_path (str): 파일 경로
+            file_type (str): 파일 타입 ('3d', 'image', 'auto')
+            
+        Returns:
+            bool: 파일 유효성
+        """
+        if not os.path.exists(file_path):
+            return False
+        
+        _, ext = os.path.splitext(file_path.lower())
+        
+        if file_type == 'auto':
+            return (ext in self.supported_3d_formats or 
+                   ext in self.supported_image_formats)
+        elif file_type == '3d':
+            return ext in self.supported_3d_formats
+        elif file_type == 'image':
+            return ext in self.supported_image_formats
+        
+        return False

src/utils/__init__.py

+"""
+유틸리티 모듈
+로깅, 성능 모니터링 등의 유틸리티 기능을 제공합니다.
+"""
+
+from .logging_utils import DetailedLogger, get_logger, reset_logger
+from .performance_monitor import (
+    PerformanceMonitor, 
+    MemoryProfiler, 
+    monitor_performance, 
+    memory_profiling,
+    get_performance_monitor,
+    get_memory_profiler
+)
+
+__all__ = [
+    'DetailedLogger',
+    'get_logger', 
+    'reset_logger',
+    'PerformanceMonitor',
+    'MemoryProfiler',
+    'monitor_performance',
+    'memory_profiling',
+    'get_performance_monitor',
+    'get_memory_profiler'
+]