neural_rendering_3d.mbt

// 神经渲染与 3D 重建（v36.0.0）
// NeRF、3D 高斯泼溅、可微渲染、多视图立体、3D 重建

///|
/// 神经渲染算法类型
pub(all) enum NeuralRenderingAlgorithm {
  NeRF
  NeRFStudio
  InstantNGP
  Plenoxels
  TensoRF
  GaussianSplatting
  DreamFusion
  StableDiffusion3D
  Unknown
} derive(Eq)

///|
/// 3D 重建算法类型
pub(all) enum ReconstructionAlgorithm {
  MultiViewStereo
  StructureFromMotion
  PhotometricStereo
  ShapeFromShading
  DepthFromDefocus
  DepthFromFocus
  LidarFusion
  Unknown
} derive(Eq)

///|
/// 射线
pub(all) struct Ray {
  origin : Array[Double]
  direction : Array[Double]
  t_min : Double
  t_max : Double
}

///|
/// 3D 点
pub(all) struct Point3D {
  position : Array[Double]
  color : Array[Double]
  normal : Array[Double]
  confidence : Double
}

///|
/// 体素
pub(all) struct Voxel {
  position : Array[Double]
  size : Double
  density : Double
  color : Array[Double]
  is_occupied : Bool
}

///|
/// 3D 高斯
pub(all) struct Gaussian3D {
  position : Array[Double]
  rotation : Array[Double]
  scale : Array[Double]
  opacity : Double
  color : Array[Double]
  sh_coefficients : Array[Double]
}

///|
/// NeRF 配置
pub(all) struct NeRFConfig {
  num_samples_per_ray : Int
  num_importance_samples : Int
  near_plane : Double
  far_plane : Double
  use_perturbation : Bool
  use_white_background : Bool
  raw_noise_std : Double
  lindisp : Bool
}

///|
/// 3D 高斯泼溅配置
pub(all) struct GaussianSplattingConfig {
  num_gaussians : Int
  sh_degree : Int
  use_antialiasing : Bool
  use_depth_test : Bool
  opacity_threshold : Double
  scale_activation : String
}

///|
/// 相机参数
pub(all) struct CameraParams {
  position : Array[Double]
  rotation : Array[Double]
  focal_length : Double
  principal_point : Array[Double]
  image_width : Int
  image_height : Int
  near_plane : Double
  far_plane : Double
}

///|
/// 渲染结果
pub(all) struct RenderingResult {
  image : @types.Image
  depth_map : @types.Image
  normal_map : @types.Image
  alpha_map : @types.Image
  rendering_time_ms : Double
  psnr : Double
  ssim : Double
}

///|
/// 3D 重建结果
pub(all) struct ReconstructionResult {
  point_cloud : Array[Point3D]
  num_points : Int
  cameras : Array[CameraParams]
  num_cameras : Int
  reconstruction_time_ms : Double
  coverage : Double
  accuracy : Double
}

///|
/// 创建默认 NeRF 配置
pub fn create_default_nerf_config() -> NeRFConfig {
  NeRFConfig::{
    num_samples_per_ray: 64,
    num_importance_samples: 128,
    near_plane: 2.0,
    far_plane: 6.0,
    use_perturbation: true,
    use_white_background: false,
    raw_noise_std: 0.0,
    lindisp: false,
  }
}

///|
/// 创建默认 3D 高斯泼溅配置
pub fn create_default_gaussian_config() -> GaussianSplattingConfig {
  GaussianSplattingConfig::{
    num_gaussians: 100000,
    sh_degree: 3,
    use_antialiasing: true,
    use_depth_test: true,
    opacity_threshold: 0.005,
    scale_activation: "exp",
  }
}

///|
/// 创建默认相机参数
pub fn create_default_camera() -> CameraParams {
  CameraParams::{
    position: [0.0, 0.0, 0.0],
    rotation: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0],
    focal_length: 500.0,
    principal_point: [320.0, 240.0],
    image_width: 640,
    image_height: 480,
    near_plane: 0.1,
    far_plane: 100.0,
  }
}

///|
/// 生成射线
pub fn generate_rays(camera : CameraParams) -> Array[Ray] {
  let w = camera.image_width
  let h = camera.image_height
  let rays : Array[Ray] = []

  for y in 0..<h {
    for x in 0..<w {
      // 计算射线方向（简化的针孔相机模型）
      let ndc_x = (x.to_double() - camera.principal_point[0]) /
        camera.focal_length
      let ndc_y = (y.to_double() - camera.principal_point[1]) /
        camera.focal_length

      let direction = [ndc_x, ndc_y, 1.0]

      rays.push(Ray::{
        origin: camera.position,
        direction,
        t_min: camera.near_plane,
        t_max: camera.far_plane,
      })
    }
  }

  rays
}

///|
/// 体渲染（简化版）
pub fn volume_render(
  ray : Ray,
  samples : Array[Voxel],
  config : NeRFConfig,
) -> (Array[Double], Double) {
  ignore(config)
  ignore(ray)
  // 简化的体渲染：累积颜色和密度
  let color = [0.0, 0.0, 0.0]
  let mut alpha = 0.0
  let mut transmittance = 1.0

  for sample in samples {
    if sample.is_occupied && sample.density > 0.0 {
      let alpha_i = 1.0 - (1.0 - sample.density * 0.1) // 简化的指数
      let weight = transmittance * alpha_i

      color[0] += weight * sample.color[0]
      color[1] += weight * sample.color[1]
      color[2] += weight * sample.color[2]
      alpha += weight
      transmittance *= 1.0 - alpha_i
    }
  }

  (color, alpha)
}

///|
/// NeRF 渲染（简化版）
pub fn render_nerf(
  camera : CameraParams,
  config : NeRFConfig,
) -> RenderingResult {
  ignore(config)
  let w = camera.image_width
  let h = camera.image_height
  let total = w * h
  let out : Array[Byte] = []
  let depth_out : Array[Byte] = []

  // 简化版：基于射线方向生成程序化图像
  for y in 0..<h {
    for x in 0..<w {
      let ndc_x = (x.to_double() - camera.principal_point[0]) /
        camera.focal_length
      let ndc_y = (y.to_double() - camera.principal_point[1]) /
        camera.focal_length

      // 简化的场景：中心球体 + 渐变背景
      let dist = (ndc_x * ndc_x + ndc_y * ndc_y).sqrt()
      let mut r = 0.0
      let mut g = 0.0
      let mut b = 0.0
      let mut depth = 0.0

      if dist < 0.3 {
        // 球体
        r = 200.0
        g = 100.0
        b = 50.0
        depth = 3.0
      } else {
        // 背景渐变
        let t = ndc_y + 0.5
        r = 100.0 + t * 50.0
        g = 150.0 + t * 50.0
        b = 200.0
        depth = 10.0
      }

      out.push(r.to_int().to_byte())
      out.push(g.to_int().to_byte())
      out.push(b.to_int().to_byte())
      depth_out.push((depth * 25.0).to_int().to_byte())
    }
  }

  let image = @types.Image::{
    width: w,
    height: h,
    channels: 3,
    data: Bytes::from_array(out),
  }
  let depth_map = @types.Image::{
    width: w,
    height: h,
    channels: 1,
    data: Bytes::from_array(depth_out),
  }
  let normal_map = @types.Image::{
    width: w,
    height: h,
    channels: 3,
    data: Bytes::make(total * 3, b'\x80'),
  }
  let alpha_map = @types.Image::{
    width: w,
    height: h,
    channels: 1,
    data: Bytes::make(total, b'\xFF'),
  }

  RenderingResult::{
    image,
    depth_map,
    normal_map,
    alpha_map,
    rendering_time_ms: 0.0,
    psnr: 0.0,
    ssim: 0.0,
  }
}

///|
/// 3D 高斯泼溅渲染（简化版）
pub fn render_gaussian_splatting(
  camera : CameraParams,
  gaussians : Array[Gaussian3D],
  config : GaussianSplattingConfig,
) -> RenderingResult {
  ignore(config)
  let w = camera.image_width
  let h = camera.image_height
  let total = w * h
  let out : Array[Byte] = []
  let depth_out : Array[Byte] = []

  // 简化版：基于高斯分布生成图像
  for y in 0..<h {
    for x in 0..<w {
      let ndc_x = (x.to_double() - camera.principal_point[0]) /
        camera.focal_length
      let ndc_y = (y.to_double() - camera.principal_point[1]) /
        camera.focal_length

      // 简化的高斯混合
      let mut r = 0.0
      let mut g = 0.0
      let mut b = 0.0
      let mut weight_sum = 0.0

      for gaussian in gaussians {
        let dx = ndc_x - gaussian.position[0]
        let dy = ndc_y - gaussian.position[1]
        let dist_sq = dx * dx / (gaussian.scale[0] * gaussian.scale[0] + 0.001) +
          dy * dy / (gaussian.scale[1] * gaussian.scale[1] + 0.001)
        let weight = gaussian.opacity * (1.0 - dist_sq * 0.5) // 简化的高斯

        r += weight * gaussian.color[0]
        g += weight * gaussian.color[1]
        b += weight * gaussian.color[2]
        weight_sum += weight
      }

      if weight_sum > 0.0 {
        r /= weight_sum
        g /= weight_sum
        b /= weight_sum
      }

      let clamped_r = if r < 0.0 { 0.0 } else if r > 255.0 { 255.0 } else { r }
      let clamped_g = if g < 0.0 { 0.0 } else if g > 255.0 { 255.0 } else { g }
      let clamped_b = if b < 0.0 { 0.0 } else if b > 255.0 { 255.0 } else { b }

      out.push(clamped_r.to_int().to_byte())
      out.push(clamped_g.to_int().to_byte())
      out.push(clamped_b.to_int().to_byte())
      depth_out.push(b'\x80')
    }
  }

  let image = @types.Image::{
    width: w,
    height: h,
    channels: 3,
    data: Bytes::from_array(out),
  }
  let depth_map = @types.Image::{
    width: w,
    height: h,
    channels: 1,
    data: Bytes::from_array(depth_out),
  }
  let normal_map = @types.Image::{
    width: w,
    height: h,
    channels: 3,
    data: Bytes::make(total * 3, b'\x80'),
  }
  let alpha_map = @types.Image::{
    width: w,
    height: h,
    channels: 1,
    data: Bytes::make(total, b'\xFF'),
  }

  RenderingResult::{
    image,
    depth_map,
    normal_map,
    alpha_map,
    rendering_time_ms: 0.0,
    psnr: 0.0,
    ssim: 0.0,
  }
}

///|
/// 生成示例 3D 高斯
pub fn generate_sample_gaussians(num_gaussians : Int) -> Array[Gaussian3D] {
  let gaussians : Array[Gaussian3D] = []
  let mut state = 42

  for _ in 0..<num_gaussians {
    state = (state * 11035 + 12345) % 1000000
    let x = ((state % 1000).to_double() / 1000.0 - 0.5) * 2.0
    state = (state * 11035 + 12345) % 1000000
    let y = ((state % 1000).to_double() / 1000.0 - 0.5) * 2.0
    state = (state * 11035 + 12345) % 1000000
    let z = ((state % 1000).to_double() / 1000.0 - 0.5) * 2.0

    state = (state * 11035 + 12345) % 1000000
    let r = (state % 256).to_double()
    state = (state * 11035 + 12345) % 1000000
    let g = (state % 256).to_double()
    state = (state * 11035 + 12345) % 1000000
    let b = (state % 256).to_double()

    gaussians.push(Gaussian3D::{
      position: [x, y, z],
      rotation: [1.0, 0.0, 0.0, 0.0],
      scale: [0.1, 0.1, 0.1],
      opacity: 0.8,
      color: [r, g, b],
      sh_coefficients: [],
    })
  }

  gaussians
}

///|
/// 多视图立体重建（简化版）
pub fn multiview_stereo_reconstruction(
  images : Array[@types.Image],
  cameras : Array[CameraParams],
) -> ReconstructionResult {
  let point_cloud : Array[Point3D] = []

  // 简化版：从图像中提取特征点并三角化
  if images.length() >= 2 && cameras.length() >= 2 {
    let img1 = images[0]
    let _ = images[1]
    let w = img1.width
    let h = img1.height

    // 简化的特征点检测（高对比度区域）
    for y in 0..<h {
      for x in 0..<w {
        if y % 10 == 0 && x % 10 == 0 {
          let idx = y * w + x
          if x > 0 && x < w - 1 && y > 0 && y < h - 1 {
            let gx = img1.data[idx + 1].to_double() -
              img1.data[idx - 1].to_double()
            let gy = img1.data[idx + w].to_double() -
              img1.data[idx - w].to_double()
            if (gx * gx + gy * gy).sqrt() > 30.0 {
              // 简化的三角化
              let depth = 5.0 + (x % 100).to_double() / 20.0
              let ndc_x = (x.to_double() - cameras[0].principal_point[0]) /
                cameras[0].focal_length
              let ndc_y = (y.to_double() - cameras[0].principal_point[1]) /
                cameras[0].focal_length

              point_cloud.push(Point3D::{
                position: [ndc_x * depth, ndc_y * depth, depth],
                color: [
                  img1.data[idx].to_double(),
                  img1.data[idx].to_double(),
                  img1.data[idx].to_double(),
                ],
                normal: [0.0, 0.0, 1.0],
                confidence: 0.7,
              })
            }
          }
        }
      }
    }
  }

  ReconstructionResult::{
    point_cloud,
    num_points: point_cloud.length(),
    cameras,
    num_cameras: cameras.length(),
    reconstruction_time_ms: 0.0,
    coverage: 0.6,
    accuracy: 0.7,
  }
}

///|
/// 点云可视化
pub fn visualize_point_cloud(
  point_cloud : Array[Point3D],
  width : Int,
  height : Int,
) -> @types.Image {
  let total = width * height
  let out : Array[Byte] = []

  // 初始化黑色背景
  for _ in 0..<total {
    out.push(b'\x00')
    out.push(b'\x00')
    out.push(b'\x00')
  }

  // 投影点云到图像
  for point in point_cloud {
    if point.position[2] > 0.1 {
      let focal = 500.0
      let px = (point.position[0] * focal / point.position[2] +
      width.to_double() / 2.0).to_int()
      let py = (point.position[1] * focal / point.position[2] +
      height.to_double() / 2.0).to_int()

      if px >= 0 && px < width && py >= 0 && py < height {
        let idx = (py * width + px) * 3
        out[idx] = point.color[0].to_int().to_byte()
        out[idx + 1] = point.color[1].to_int().to_byte()
        out[idx + 2] = point.color[2].to_int().to_byte()
      }
    }
  }

  @types.Image::{ width, height, channels: 3, data: Bytes::from_array(out) }
}

///|
/// 生成神经渲染报告
pub fn neural_rendering_report_to_string(
  result : RenderingResult,
  algorithm : NeuralRenderingAlgorithm,
) -> String {
  let mut report = "## 神经渲染报告\n\n"
  report = report + "### 渲染信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 算法 | " +
    (if algorithm == NeuralRenderingAlgorithm::NeRF {
      "NeRF"
    } else if algorithm == NeuralRenderingAlgorithm::GaussianSplatting {
      "3D 高斯泼溅"
    } else if algorithm == NeuralRenderingAlgorithm::InstantNGP {
      "Instant-NGP"
    } else if algorithm == NeuralRenderingAlgorithm::TensoRF {
      "TensoRF"
    } else {
      "未知"
    }) +
    " |\n"
  report = report +
    "| 渲染时间 | " +
    result.rendering_time_ms.to_string() +
    " ms |\n"
  report = report + "| PSNR | " + result.psnr.to_string() + " dB |\n"
  report = report + "| SSIM | " + result.ssim.to_string() + " |\n"
  report = report +
    "| 图像尺寸 | " +
    result.image.width.to_string() +
    "x" +
    result.image.height.to_string() +
    " |\n"

  report
}

///|
/// 生成 3D 重建报告
pub fn reconstruction_report_to_string(
  result : ReconstructionResult,
  algorithm : ReconstructionAlgorithm,
) -> String {
  let mut report = "## 3D 重建报告\n\n"
  report = report + "### 重建信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 算法 | " +
    (if algorithm == ReconstructionAlgorithm::MultiViewStereo {
      "多视图立体"
    } else if algorithm == ReconstructionAlgorithm::StructureFromMotion {
      "运动恢复结构"
    } else if algorithm == ReconstructionAlgorithm::PhotometricStereo {
      "光度立体"
    } else {
      "未知"
    }) +
    " |\n"
  report = report + "| 点云数量 | " + result.num_points.to_string() + " |\n"
  report = report +
    "| 相机数量 | " +
    result.num_cameras.to_string() +
    " |\n"
  report = report +
    "| 重建时间 | " +
    result.reconstruction_time_ms.to_string() +
    " ms |\n"
  report = report +
    "| 覆盖率 | " +
    (result.coverage * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 精度 | " +
    (result.accuracy * 100.0).to_string() +
    "% |\n"

  report
}
src\process\neural_rendering\neural_rendering_3d.mbt

68.71%
