MVS學習（二）：MVS重建的數據獲取方法推薦

• 相機模型的精度
• 圖像分辨率
• 圖像重疊度
• 圖像數量
• 圖像質量

MVS學習（一）：綜述論文Multi-View Stereo: A Tutorial閱讀記錄
Image acquisition is the first critical step for successful MVS。
無論是三維重建任務，還是其他各種cv相關任務， 圖像獲取的質量往往決定功能效果的上限，也是對效果影響最大的因素，但是實際執行過程中往往容易被忽略，或者不知道從哪些角度考慮去優化圖像質量，下文說明針對mvs任務，如何獲取符合要求的高質量圖像。

相機模型的精度

Accuracy of the camera models: MVS techniques are highly dependent on the accuracy of the camera parameters. Typical reprojection error RMSE should be sub-pixel, ideally smaller than 0.5 pixels. In case reprojection error is large, one possibility is to shrink the input images and modify the corresponding camera parameters, which will reduce the reprojection error proportionally to the shrinkage ratio

這里主要說明相機參數對mvs結果的影響。mvs之所以目前效果有很多提升很大的愿意就是以sfm為代表的相機參數獲取方法質量的提升，所以如何獲取更高精度的相機參數是優化最終mvs效果的一個思考方向。

圖像分辨率

High resolution images bring up details that can be used to uniquely identify a pixel from
its neighbors, thus improving the correspondence cue used by MVS algorithms to find similar pixels across multiple images

這里說明了為什么像素點多對重建效果有提升：更多的像素點有利于發現與鄰近像素點之間的差異（低解析度可以理解成在高解析度圖片的下采樣，細節信息肯定有損失），細節信息豐富有利于重建時候在多張圖像中尋找定位相似的像素點（高分辨率的影響也可以理解成紋理信息更豐富）

Note however that by resolution we do not mean just having lots of pixels, the quality of camera lens also matters. Having a very high-res image captured with a poor quality lens will not improve results, and it may actually make them worse, e.g. due to worse results at the SfM stage.

分辨率有一個點值得注意，即并不是分辨率越高（像素點越多）最終的重建效果越好。相機的鏡頭質量更重要，相機鏡頭的畸變等會降低sfm的效果，單純提升分辨率而鏡頭質量差會導致更差的結果，sfm對最終效果的影響巨大。

圖像重疊度

For MVS to work correctly, multiple images need to see the same piece of geometry from multiple view points

圖像數量

圖像數量越過通常效果越好，但是很多實際場景應用必須在圖像數量和分辨率之間做平衡

However, if one has
to choose between image resolution and number of images, there is no easy decision. MVS algorithms reconstruct more details from higher resolution images, as MVS suffers little from ambiguous matches. On the other hand, high resolution images become an issue for SfM, as the so-called ratio-test would reject many feature matches. Therefore, if good camera calibration is available, in general one should choose image resolution over number of images

更多的圖像數量對mvs基本都是積極影響，但是可能會導致sfm算法的精度下降，所以，當分辨率和圖像數量必須權衡的時候，優先保證分辨率。

圖像質量

Although there exist many algorithms that are robust to illumination variations across the images, the more stable it can be,the better. For example, flash changes shading and shadowing effects in every image, and should not be used for weakly textured surfaces

盡管算法對光照變化有一定的魯棒性，但是如果可以保證光照穩定還是要盡量保證。光照越穩定，通常結果越好。尤其對于貧紋理表面物體更要保證光照的穩定性。

Ideally all，the images used in MVS should be all-in-focus. This can be achieved
by using small apertures and large exposures, within the limits of a particular application.

理論上，用于重建的圖片要求全對焦，也就是物體要處于相機的焦點上。這對于小物體還好保證，大場景如何做也有很多實際的改進。
MVS學習（一）：綜述論文Multi-View Stereo: A Tutorial閱讀記錄

總結

以上是生活随笔為你收集整理的MVS学习（二）：MVS重建的数据获取方法推荐的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。