高反射金屬表面於結構光三維量測時，容易因鏡面反射造成局部圖像飽和或暗區條紋對比不足，使編碼資訊遺失，進而產生點雲空洞與幾何重建誤差。為改善既有偽曝光類高動態範圍（High Dynamic Range, HDR）方法中參考灰階值多仰賴固定設定或經驗選取之限制，本研究提出一套結合機器學習灰階預測之自適應偽曝光類 HDR 結構光三維掃描系統。
本研究首先投影全白均勻圖像並擷取工件反射圖像，利用灰階直方圖之主要峰值與谷值分析表面反射特性，以判斷所需之中間參考灰階圖像組數與目標等效曝光條件。其後，系統先於預先設定之低曝光條件下擷取結構光圖像，並進行初步解碼檢查，以確認高反射區域未發生明顯飽和且仍保有可用之編碼資訊；確認後，再於高曝光條件下擷取結構光圖像並進行解碼檢查，以確認低反射或暗區具有足夠之條紋對比。接著，本研究將曝光資訊與圖像灰階特徵輸入 XGBoost 回歸模型，以預測各中間參考圖像所需之投影灰階值。最後，利用高、低曝光圖像與參考圖像建立 Gamma 對應關係，生成偽曝光圖像，並透過多曝光拉普拉斯金字塔融合完成解碼與三維點雲重建。
實驗以六個金屬工件建立投影灰階預測資料集，並比較 SVR、Random Forest、MLP 與 XGBoost 等回歸模型。結果顯示，XGBoost之MAE為0.191灰階、RMSE為0.787 灰階，整體預測表現最佳。於平板、圓柱及圓盤金屬工件之方法比較中，本研究方法分別獲得 0.077 mm、0.073 mm 及 0.176mm 之幾何擬合殘差標準差，均較兩種比較方法具有較低的幾何擬合殘差或較高的有效點雲數量。結果證實，本研究可依工件表面反射特性，自適應預測參考投影灰階值，並提升高反射金屬表面之結構光解碼穩定性與三維重建品質。

Highly reflective metal surfaces often cause local image saturation or insufficient fringe contrast in dark regions during structured-light 3D measurement, leading to decoding failure, point cloud holes, and geometric reconstruction errors. To overcome the limitation of existing pseudo-exposure-based high dynamic range (HDR) methods, in which the reference grayscale values are generally fixed or empirically selected, this study proposes an adaptive pseudo-exposure-based HDR structured-light 3D scanning system using machine-learning-based grayscale prediction.
A uniform white image is first projected onto the workpiece, and the reflected image is captured to analyze the surface brightness distribution using a grayscale histogram. Based on this analysis, the required number of intermediate reference images and the target equivalent exposure conditions are determined. Structured-light images are then captured under low- and high-exposure conditions, followed by preliminary decoding to confirm that usable encoded information is preserved. Exposure information and grayscale image features are subsequently input into an XGBoost regression model to predict the projection grayscale values for the intermediate reference images. Finally, Gamma mapping relationships are established using the low-exposure, high-exposure, and reference images to generate pseudo-exposure images, which are fused through multi-exposure Laplacian pyramid fusion for decoding and 3D point cloud reconstruction.
Experiments were conducted on three types of metal workpieces, including flat, cylindrical, and disk-shaped workpieces, and the proposed method was compared with two existing methods. The results show that the proposed method reduced the geometric fitting residuals and increased the number of valid point cloud data for all three types of workpieces. These results demonstrate that the proposed method can adaptively predict reference projection grayscale values according to the surface reflectance characteristics of the workpiece, thereby improving the decoding stability and 3D reconstruction quality of structured-light scanning for highly reflective metal surfaces.

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