Manet: Multi-Scale Aggregated Network For Light Field Depth Estimation

Abstract

We present a novel end-to-end network, MANet, for light field depth estimation. MANet is a parameter-effective and efficient multi-scale aggregated network, which is about 3 times smaller and 3 times faster than the current top-performing method Epinet. The MANet architecture is performed for estimating depth from light field plenoptic cameras, and experimental results show that the proposed MANet outperforms state-of-the-art methods on HCI, CVIA-HCI and EPFL Lytro light field datasets.

Depth Estimation for Integral Imaging Microscopy Using a 3D–2D CNN with a Weighted Median Filter

“…We compared RMSE values with the state-of-the-art algorithms. In these cases, our method is compared with EPI-ORM [ 32 ], EPINET [ 27 ], MANET [ 38 ], and 3D-CNN-LF Depth [ 39 ]. The results of the qualitative evaluation (boxes, dino, dots, pyramids, and town) are shown in Table 1 .…”

“…To evaluate our method for estimating light-field depth, we compared it with state-of-the-art algorithms. In these cases, EPI-ORM [ 32 ], EPINET [ 27 ], MANET [ 38 ], and 3D-CNN-LF Depth [ 39 ] were quantified with a discrete entropy (DE) test. All specimens are shown in Figure 7 .…”

“…This network uses a sequence layer ‘Conv-Relu-Conv-BN-Relu’. A multi-stream depth network combining cost volume and 3D aggregation was presented by Li et al [ 38 ]. Fsluvegi et al [ 39 ] proposed a fully connected CNN network with two streams as well as augmentation methods for tackling the overfitting issue.…”

“…( a ) Central directional view image. Depth images generated by ( b ) EPI-ORM [ 32 ], ( c ) EPINET [ 27 ], ( d ) MANET [ 38 ], ( e ) 3D-CNN-LF Depth [ 39 ], ( f ) proposed methods, from the same input images.…”

“… Discrete entropy values for gear, microchip, and seedpod using EPI-ORM [ 32 ], EPINET [ 27 ], MANET [ 38 ], and 3D-CNN-LF Depth [ 39 ] proposed depth estimation methods. …”