SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation

Michaël Ramamonjisoa and Vincent Lepetit

We introduce SharpNet, a method that predicts an accurate depth map for an input color image, with a particular attention to the reconstruction of occluding contours: Occluding contours are an important cue for object recognition, and for realistic integration of virtual objects in Augmented Reality, but they are also notoriously difficult to reconstruct accurately. For example, they are a challenge for stereo-based reconstruction methods, as points around an occluding contour are visible in only one image. Inspired by recent methods that introduce normal estimation to improve depth prediction, we introduce a novel term that constrains depth and occluding contours predictions. Since ground truth depth is difficult to obtain with pixel-perfect accuracy along occluding contours, we use synthetic images for training, followed by fine-tuning on real data. We demonstrate our approach on the challenging NYUv2-Depth dataset, and show that our method outperforms the state-of-the-art along occluding contours, while performing on par with the best recent methods for the rest of the images. Its accuracy along the occluding contours is actually better than the ''ground truth'' acquired by a depth camera based on structured light. We show this by introducing a new benchmark based on NYUv2-Depth for evaluating occluding contours in monocular reconstruction, which is our second contribution.

Title = {SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation},
Author = {M. Ramamonjisoa and V. Lepetit},
Journal = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
Year = {2019}

Published in International Conference on Computer Vision (ICCV) Workshop on 3D Reconstruction in the Wild, 2019

On Object Symmetries and 6D Pose Estimation from Images

Giorgia Pitteri*, Michaël Ramamonjisoa*, Slobodan Ilic and Vincent Lepetit
* Denotes equal contribution.

Objects with symmetries are common in our daily life and in industrial contexts, but are often ignored in the recent literature on 6D pose estimation from images. In this paper, we study in an analytical way the link between the symmetries of a 3D object and its appearance in images. We explain why symmetrical objects can be a challenge when training machine learning algorithms that aim at estimating their 6D pose from images. We propose an efficient and simple solution that relies on the normalization of the pose rotation. Our approach is general and can be used with any 6D pose estimation algorithm. Moreover, our method is also beneficial for objects that are 'almost symmetrical', mph{i.e.} objects for which only a detail breaks the symmetry. We validate our approach within a Faster-RCNN framework on a synthetic dataset made with objects from the T-Less dataset, which exhibit various types of symmetries, as well as real sequences from T-Less.

Title = {On Object Symmetries and 6D Pose Estimation from Images},
Author = {G. Pitteri and M. Ramamonjisoa and S. Ilic and V. Lepetit},
Journal = {International Conference on 3D Vision},
Year = {2019}

Published in 2019 International Conference on 3D Vision (3DV), 2019