3d shapes faces edge的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列免費下載的地點或者是各式教學

全身性影像辨識與人臉圖像QR Code之雙重身分識別研究

為了解決3d shapes faces edge的問題，作者王啟宏 這樣論述：

防疫期間病患至醫院診療的過程中，難免需近距離接觸來確認身分，這將帶來高風險的傳染源，因此須導入人臉辨識系統與改善傳統醫療手環的方案予以因應。現有的文獻中，多數身分識別系統侷限於近距離偵測，且人臉特徵產生變化時會難以辨識身分。因此，我們建構雙重認證系統，第一道認證，利用Kinect擷取病患的人臉與全身影像資訊，接續進行機器學習、CNN網路模型及人體骨架辨識的整合功能，可提供性別、預估年齡並結合肩部與身高座標的多程序判斷，以提升身分辨識的準確度。第二道認證，將QR Code結合人臉影像，並利用AM網點的固定密度特性與Error Diffusion技術，創造不可偽造的人臉圖像醫療手環，亦可

透過手機掃描來確認病患資料。 此外，人臉影像的前置處理也會影響性別、年齡及身分識別的準確率，經實驗證明，對於性別辨識準確率可達90.4%、年齡估計為80.8%、身分識別為96%，而人臉圖像化QR Code的最佳讀取效能為0.55秒，由此看出本研究的整合功能性較其它文獻完整。

從稀疏點雲和彩色圖像進行 3D 對象檢測的深度補全

為了解決3d shapes faces edge的問題，作者賴胡 這樣論述：

This thesis presents a deep learning-based 3D object detection framework which is one of the fundamental components in autonomous driving. The use of LiDAR is indispensable to achieve precise depth predictions. A key challenge of this approach is how to efficiently localize the objects in the point

cloud of large-scale scenes. Our method starts with depth completion. Instead of solely relying on 3D proposals, we will take leverages of depth completion which greatly increases the density of point cloud. Basically, depth completion faces 2 major challenges, first, irregularly distributed points

in point cloud: it is difficult to fuse information from multi-sensors, such as the fusion of point cloud and color images. Second, the popular dataset, i.e., KITTI provides the ground truth depth map but only 30% of pixels contain valid depth values, which makes it a challenge to train good models

for the task of depth completion. To overcome the mentioned challenges, we train a model in a self-supervised manner to get better results. Our depth completion prediction outperforms many state-of-the-art methods in terms of RMSE, MAE, IRMSE, and IMAE. Then we take advantage of dense depth map by

projecting back to 3D coordinates and obtain a denser point cloud to achieve a better 3D object detection. Many 3D object detectors are adopted to verify the efficiency of our dense depth map. Experimental results show the improvement brought by the denser point cloud.