I ) EL 3 3JF GK 3 D ) EL ( I C I EDP G . - E DI EJ ] 3 D ) EL rv G I :EG E N ED I D N E c wi JF GK GD D c n O F G D FI ) EL IE 3 D ) EL GEJ FI (MF D EDP G . - dfdghe] c FI ) EL csm Wb 3 D ) EL x FI (MF D EDSo 3 D ) EL rv TU ul]rv apty[rvc n 0 2 2 0 5
2C , . 0 E C 2 1 n 0 LhpA D 4 0 0 0 4 dl n 0 G sb Lo o e n , C C2B C9 C C 2 E ➤ B E 4 4 0 O f n dl ti n 0 Ira G 7 c w Scene flow Optical flow R EPC++ T n dluv [C. Luo et. al., 2018]
CD D , 3 12 S. Wu et. al., “Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild”, CVPR 2020 ! scene flow & depth "
P hMd e . 1, L 1 h i M Cb hMag h . 1, M c , 19 Photometric loss Smoothness loss Synthesized left image Occlusion mask ➤ Disparity loss occlusion mask disparity map Left image SSIM L1
d 1 . ,. . 1 . 2, -2 2 ,. . e L bC L b a b L 1 . 2, ,, -.- T S LM 21 Scene flow photometric loss 3D point reconstruction loss Smoothness loss Synthesized t image Occlusion mask ➤ occlusion mask flow t image ➤ Edge-aware 2nd order smoothness Depth Optical flow
T . . aS , c aSD d b , , c . 1 eD 22 Scene flow photometric loss 3D point reconstruction loss Smoothness loss d(t+1)! Occlusion mask d(t) Scene flow ! ➤ Edge-aware 2nd order smoothness ➤ depth map " scene flow
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