CSM 3D：AI三维生成方案

CSM 3D生成管线模拟模拟单图深度估计、3D网格重建、网格优化和纹理映射的完整流程"""import numpy as npfrom collections import defaultdict # === 1. 单目深度估计 ===class MonoDepthEstimator:    """模拟单目深度估计网络"""    def __init__(self, img_size=256):        self.img_size = img_size     def estimate_depth(self, image_2d):        """从2D图像推断深度图"""        h, w = image_2d.shape        depth_map = np.zeros((h, w))        for y in range(h):            for x in range(w):                # 模拟深度估计: 物体中心近, 边缘远                cy, cx = h / 2, w / 2                dist_from_center = np.sqrt((y - cy) ** 2 + (x - cx) ** 2)                max_dist = np.sqrt(cy ** 2 + cx ** 2)                # 前景(亮区)近, 背景(暗区)远                brightness = image_2d[y, x] / 255.0                base_depth = 1.0 - dist_from_center / max_dist * 0.5                depth_map[y, x] = base_depth * 0.7 + brightness * 0.3        return depth_map     def depth_to_point_cloud(self, depth_map, fov=60):        """将深度图转换为3D点云"""        h, w = depth_map.shape        fx = fy = (w / 2) / np.tan(np.radians(fov / 2))        points = []        for y in range(h):            for x in range(w):                z = depth_map[y, x]                px = (x - w / 2) * z / fx                py = (y - h / 2) * z / fy                pz = z                points.append([px, py, pz])        return np.array(points)  # === 2. 3D网格重建 ===class MeshReconstructor:    """从点云重建3D网格"""    def __init__(self):        self.vertices = []        self.faces = []     def poisson_reconstruction(self, point_cloud, resolution=64):        """模拟泊松表面重建"""        # 简化: 将点云体素化并提取等值面        n_points = len(point_cloud)        # 模拟生成顶点和面        grid_size = int(np.cbrt(n_points / 10))        grid_size = max(8, min(grid_size, resolution))        # 生成顶点网格        for i in range(grid_size):            for j in range(grid_size):                for k in range(grid_size):                    if np.random.random() > 0.3:                        self.vertices.append([                            i / grid_size - 0.5,                            j / grid_size - 0.5,                            k / grid_size - 0.5                        ])        # 生成三角面        n_verts = len(self.vertices)        for i in range(0, n_verts - 3, 3):            self.faces.append([i, i + 1, i + 2])        return {            "vertices": len(self.vertices),            "faces": len(self.faces),            "resolution": grid_size,            "method": "poisson"        }     def back_side_completion(self, front_mesh_info):        """模拟背面补全: 基于对称性推测背面几何"""        front_verts = front_mesh_info.get("vertices", 0)        # 模拟: 通过对称镜像生成背面顶点        back_verts = int(front_verts * 0.85)  # 背面略少        return {            "front_vertices": front_verts,            "back_vertices": back_verts,            "method": "symmetric_mirror + AI_inpainting",            "confidence": "背面为AI推测结果, 可能与实物有差异"        }  # === 3. 网格优化 ===class MeshOptimizer:    """网格质量优化器"""    def __init__(self):        pass     def simplify(self, vertex_count, target_ratio=0.3):        """网格简化: 减少面数到目标比例"""        target = int(vertex_count * target_ratio)        return {            "original_vertices": vertex_count,            "target_vertices": target,            "reduction": f"{(1 - target_ratio) * 100:.0f}%",            "method": "quadric_edge_collapse",            "quality_loss": f"{target_ratio * 0.1:.2f}"        }     def retopology(self, mesh_info):        """重拓扑: 优化网格布线"""        return {            "original_topology": "irregular",            "new_topology": "quad_dominant",            "edge_loops_added": True,            "poles_optimized": True,            "deformation_ready": True,            "note": "重拓扑后网格支持动画变形, 可直接用于角色动画"        }     def generate_lod(self, vertex_count, levels=3):        """生成LOD (Level of Detail)"""        lods = []        for i in range(levels):            ratio = 1.0 / (2 ** i)            lods.append({                "level": i,                "vertices": int(vertex_count * ratio),                "target_distance": f"{10 * (i + 1)}m",                "use_case": "近景" if i == 0 else ("中景" if i == 1 else "远景")            })        return lods     def uv_optimize(self, vertex_count):        """UV映射优化"""        return {            "uv_islands": min(vertex_count // 100, 16),            "texel_density": "1024x1024",            "overlaps_fixed": True,            "stretching": "minimal",            "packing_efficiency": "85%"        }  # === 4. 纹理映射与材质生成 ===class TextureGenerator:    """纹理映射与PBR材质生成"""    def __init__(self):        pass     def project_texture(self, image_2d, uv_coords):        """将输入图像纹理投影到3D模型表面"""        h, w = image_2d.shape        texture_coverage = 0.0        for uv in uv_coords:            u, v = uv            px = int(u * w)            py = int(v * h)            if 0 <= px < w and 0 <= py < h:                texture_coverage += 1        coverage = texture_coverage / len(uv_coords) if uv_coords else 0        return {            "method": "UV_projection",            "coverage": round(coverage, 4),            "visible_area": "直接从输入图像投影",            "invisible_area": "需AI生成补全"        }     def generate_back_texture(self, front_texture_info):        """生成背面纹理: AI推测补全"""        return {            "method": "diffusion_model_inpainting",            "source": "基于正面纹理的AI推测",            "confidence": "medium",            "limitations": ["金属反光还原有限", "透明材质效果不佳", "光照影响纹理准确性"],            "note": "背面纹理为AI推测, 非真实重建结果"        }     def infer_pbr_materials(self, image_2d):        """推断PBR材质参数"""        h, w = image_2d.shape        avg_brightness = np.mean(image_2d) / 255.0        # 模拟PBR参数推断        return {            "albedo": round(avg_brightness, 3),            "roughness": round(1.0 - avg_brightness * 0.5, 3),            "metallic": 0.1 if avg_brightness > 0.5 else 0.3,            "normal_map": "auto_generated_from_geometry",            "ao_map": "auto_generated",            "note": "PBR参数来自图像分析, 受光照和拍摄角度影响"        }  # === 5. 导出与集成 ===class ModelExporter:    """3D模型导出器"""    FORMATS = ["GLB", "OBJ", "FBX", "USD", "STL"]     def export(self, mesh_info, texture_info, format="GLB"):        """导出3D模型"""        if format not in self.FORMATS:            return {"error": f"Unsupported format: {format}"}        file_size_mb = mesh_info.get("vertices", 1000) * 0.0001        return {            "format": format,            "file_size_mb": round(file_size_mb, 2),            "vertices": mesh_info.get("vertices", 0),            "faces": mesh_info.get("faces", 0),            "textures_included": True,            "pbr_materials": True,            "engine_compatible": ["Unity", "Unreal Engine", "Three.js", "WebAR"],            "generation_time_s": 30        }     def batch_process(self, image_count, format="GLB"):        """批量处理"""        total_time = image_count * 30  # 每张30秒        return {            "total_images": image_count,            "format": format,            "estimated_time_min": round(total_time / 60, 1),            "avg_per_model_s": 30,            "output_dir": "/output/models/",            "success_rate": "95%"        }  # === 演示运行 ===print("=" * 60)print("CSM 3D生成管线模拟")print("=" * 60) print("\n--- 1. 单目深度估计 ---")depth_estimator = MonoDepthEstimator(img_size=64)# 模拟一张64x64的产品白底图image = np.random.randint(100, 255, (64, 64))image[16:48, 16:48] = 200  # 中心物体区域更亮depth_map = depth_estimator.estimate_depth(image)print(f"  输入图像: 64x64")print(f"  深度图范围: {depth_map.min():.3f} ~ {depth_map.max():.3f}")point_cloud = depth_estimator.depth_to_point_cloud(depth_map)print(f"  点云数量: {len(point_cloud)}") print("\n--- 2. 3D网格重建 ---")reconstructor = MeshReconstructor()mesh_info = reconstructor.poisson_reconstruction(point_cloud, resolution=32)print(f"  顶点数: {mesh_info['vertices']}")print(f"  面数: {mesh_info['faces']}")back_info = reconstructor.back_side_completion(mesh_info)print(f"  背面补全: {back_info['back_vertices']}顶点 ({back_info['method']})")print(f"  置信度: {back_info['confidence']}") print("\n--- 3. 网格优化 ---")optimizer = MeshOptimizer()simplified = optimizer.simplify(mesh_info["vertices"], target_ratio=0.3)print(f"  简化: {simplified['original_vertices']} -> {simplified['target_vertices']} ({simplified['reduction']}减少)")retopo = optimizer.retopology(mesh_info)print(f"  重拓扑: {retopo['original_topology']} -> {retopo['new_topology']}")lods = optimizer.generate_lod(mesh_info["vertices"], levels=3)for lod in lods:    print(f"  LOD{lod['level']}: {lod['vertices']}顶点 ({lod['use_case']})")uv = optimizer.uv_optimize(mesh_info["vertices"])print(f"  UV: {uv['uv_islands']}个岛, 拉伸={uv['stretching']}") print("\n--- 4. 纹理与材质 ---")tex_gen = TextureGenerator()uv_coords = [(np.random.random(), np.random.random()) for _ in range(100)]tex_proj = tex_gen.project_texture(image, uv_coords)print(f"  纹理投影覆盖: {tex_proj['coverage']:.1%}")back_tex = tex_gen.generate_back_texture(tex_proj)print(f"  背面纹理: {back_tex['method']}")pbr = tex_gen.infer_pbr_materials(image)print(f"  PBR: albedo={pbr['albedo']}, roughness={pbr['roughness']}, metallic={pbr['metallic']}") print("\n--- 5. 导出 ---")exporter = ModelExporter()export_result = exporter.export(mesh_info, tex_proj, format="GLB")print(f"  格式: {export_result['format']}")print(f"  文件大小: {export_result['file_size_mb']}MB")print(f"  引擎兼容: {', '.join(export_result['engine_compatible'])}")batch = exporter.batch_process(20000, format="GLB")print(f"  批量处理: {batch['total_images']}张, 预计{batch['estimated_time_min']}分钟") print("\n" + "=" * 60)print("3D生成完成")print("=" * 60)