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.+However%252C+loss+functions+applied+for+deep+MVS+are+rarely+studied.+In+this+paper%252C+we+first+analyze+existing+loss+functions%27+properties+for+deep+depth+based+MVS+approaches.+Regression+based+loss+leads+to+inaccurate+continuous+results+by+computing+mathematical+expectation%252C+while+classification+based+loss+outputs+discretized+depth+values.+To+this+end%252C+we+then+propose+a+novel+loss+function%252C+named+adaptive+Wasserstein+loss%252C+which+is+able+to+narrow+down+the+difference+between+the+true+and+predicted+probability+distributions+of+depth.+Besides%252C+a+simple+but+effective+offset+module+is+introduced+to+better+achieve+sub-pixel+prediction+accuracy.+Extensive+experiments+on+different+benchmarks%252C+including+DTU%252C+Tanks+and+Temples+and+BlendedMVS%252C+show+that+the+proposed+method+with+the+adaptive+Wasserstein+loss+and+the+offset+module+achieves+state-of-the-art+performance.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.c50ada3cb114a4fd141abda52ac80da3%3frik%3desq8Z9qYII2UPw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Multi-Perspective+Stereo+Modeling+Algorithm&FORM=IRPRST&ck=73752D485DCABA03FAAF57112E918AAD&selectedIndex=27&itb=0)
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