3 accepted papers
We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Euclidean and Similarity group of transformations. We leverage on the representational power of convolutional neural networks to compute these geometric quantities. In comparison wit…
The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research. Yet, the precision of existing depth scanners is not accurate enough to recover fine details of a scanned object. While modern shading based depth refinement methods have been proven to work well with L…
The popularity of low-cost RGB-D scanners is increasing on a daily basis. Nevertheless, existing scanners often cannot capture subtle details in the environment. We present a novel method to enhance the depth map by fusing the intensity and depth information to create more detailed range profiles. T…