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.

本文介绍了 SharpNet，一种用于预测准确的深度图像的方法，该方法对于重构遮挡轮廓尤其关注，通过使用带有相机的合成图像进行训练，其在挡住轮廓方面的准确度实际上优于使用结构光的深度相机获取的真实数据。