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## Section: New Results

### Visual Data Analysis

Scene depth, Scene flows, 3D modeling, Light-fields, 3D point clouds

#### Scene depth estimation from light fields

Participants : Christine Guillemot, Xiaoran Jiang, Jinglei Shi.

While there exist scene depth estimation methods, these methods, mostly designed for stereo content or for pairs of rectified views, do not effectively apply to new imaging modalities such as light fields. We have focused on the problem of scene depth estimation for every viewpoint of a dense light field, exploiting information from only a sparse set of views [24]. This problem is particularly relevant for applications such as light field reconstruction from a subset of views, for view synthesis, for 3D modeling and for compression. Unlike most existing methods, the proposed algorithm computes disparity (or equivalently depth) for every viewpoint taking into account occlusions. In addition, it preserves the continuity of the depth space and does not require prior knowledge on the depth range.

We have then proposed a learning based depth estimation framework suitable for both densely and sparsely sampled light fields. The proposed framework consists of three processing steps: initial depth estimation, efficient fusion with occlusion handling and refinement. The estimation can be performed from a flexible subset of input views. The fusion of initial disparity estimates, relying on two warping errors measures, allows us to have an accurate estimation in occluded regions and along the contours. The use of trained neural networks has the advantage of a limited computational cost at estimation time. In contrast with methods relying on the computation of cost volumes, the proposed approach does not need any prior information on the disparity range. Experimental results show that the proposed method outperforms state-of-the-art light fields depth estimation methods for a large range of baselines [15].

The training of the proposed neural networks based architecture requires having ground truth disparity (or depth) maps. Although a few synthetic datasets exist for dense light fields with ground truth depth maps, no such dataset exists for sparse light fields with large baselines. This lack of training data with ground truth depth maps is a crucial issue for supervised learning of neural networks for depth estimation. We therefore created two datasets, namely SLFD and DLFD, containing respectively sparsely sampled and densely sampled synthetic light fields. To our knowledge, SLFD is the first available dataset providing sparse light field views and their corresponding ground truth depth and disparity maps. The created datasets have been made publicly available together with the code and the trained models.

#### Scene flow estimation from light fields

Participants : Pierre David, Christine Guillemot.

We have addressed the problem of scene flow estimation from sparsely sampled video light fields. Scene flows can be seen as 3D extensions of optical flows by also giving the variation in depth along time in addition to the optical flow. Scene flows are tools needed for temporal processing of light fields. Estimating dense scene flows in light fields poses obvious problems of complexity due to the very large number of rays or pixels. This is even more difficult when the light field is sparse, i.e., with large disparities, due to the problem of occlusions. The developments in this area are also made difficult due to the lack of test data, i.e., there is no publicly available synthetic video light fields with the corresponding ground truth scene flows. In order to be able to assess the performance of the proposed method, we have therefore created synthetic video light fields from the MPI Sintel dataset. This video light field data set has been produced with the Blender software by creating new production files placing multiple cameras in the scene, controlling the disparity between the set of views.

We have then developed a local 4D affine model to represent scene flows, taking into account light field epipolar geometry. The model parameters are estimated per cluster in the 4D ray space. We have first developed a sparse to dense estimation method that avoids the difficulty of computing matches in occluded areas [18], which we have further extended by developing a dense scene flow estimation method from light fields. The local 4D affine parameters are in this case derived by fitting the model on initial motion and disparity estimates obtained by using 2D dense optical flow estimation techniques.

We have shown that the model is very effective for estimating scene flows from 2D optical flows (see Fig.2). The model regularizes the optical flows and disparity maps, and interpolates disparity variation values in occluded regions. The proposed model allows us to benefit from deep learning-based 2D optical flow estimation methods while ensuring scene flow geometry consistency in the 4 dimensions of the light field.

Figure 2. Visual comparison of our method with respect to reference methods (PWC-Net: deep learning method for optical flow estimation; oriented light field window (OLFW), Piece-wise Rigid Scene Model (PRSM), Object Scene Flow (OSF)). First row: optical flows; Second row: disparity maps; Third row: disparity variations. The red pixels are the occlusion mask where there is no ground truth disparity variation available.

#### Depth estimation at the decoder in the MPEG-I standard

Participants : Patrick Garus, Christine Guillemot, Thomas Maugey.

#### Spherical feature extraction for 360 light field reconstruction from omni-directional fish-eye camera captures

Participants : Christine Guillemot, Fatma Hawary, Thomas Maugey.

With the increasing interest in wide-angle or 360° scene captures, the extraction of descriptors well suited to the geometry of this content is a key problem for a variety of processing tasks. Algorithms designed for feature extraction in 2D images are hardly applicable to 360${}^{\circ }$ images or videos as they do not well take into account their specific spherical geometry. To cope with this difficulty, it is quite common to perform an equirectangular projection of the spherical content, and to compute spherical features on projected and stitched content. However, this process introduces geometrical distortions with implications on the accuracy of applications such as angle estimation, depth calculation and 3D scene reconstruction. We adapt a spherical feature descriptor to the geometry of fish-eye cameras that avoids equirectangular projection. The captured image is directly mapped onto a spherical model of the 360${}^{\circ }$ camera. In order to evaluate the interest of the proposed fish-eye adapted descriptor, we consider the angular coordinates of feature points on the sphere. We assess the stability of the corresponding angles when capturing the scene by a moving fish-eye camera. Experimental results show that the proposed fish-eye adapted descriptor allows a more stable angle estimation, hence a more robust feature detection, compared to spherical features on projected and stitched contents.