2 research outputs found
A study of the effect of the illumination model on the generation of synthetic training datasets
The use of computer generated images to train Deep Neural Networks is a
viable alternative to real images when the latter are scarce or expensive. In
this paper, we study how the illumination model used by the rendering software
affects the quality of the generated images. We created eight training sets,
each one with a different illumination model, and tested them on three
different network architectures, ResNet, U-Net and a combined architecture
developed by us. The test set consisted of photos of 3D printed objects
produced from the same CAD models used to generate the training set. The effect
of the other parameters of the rendering process, such as textures and camera
position, was randomized.
Our results show that the effect of the illumination model is important,
comparable in significance to the network architecture. We also show that both
light probes capturing natural environmental light, and modelled lighting
environments, can give good results. In the case of light probes, we identified
as two significant factors affecting performance the similarity between the
light probe and the test environment, as well as the light probe's resolution.
Regarding modelled lighting environment, similarity with the test environment
was again identified as a significant factor.Comment: 8 page
DronePose: Photorealistic UAV-Assistant Dataset Synthesis for 3D Pose Estimation via a Smooth Silhouette Loss
In this work we consider UAVs as cooperative agents supporting human users in
their operations. In this context, the 3D localisation of the UAV assistant is
an important task that can facilitate the exchange of spatial information
between the user and the UAV. To address this in a data-driven manner, we
design a data synthesis pipeline to create a realistic multimodal dataset that
includes both the exocentric user view, and the egocentric UAV view. We then
exploit the joint availability of photorealistic and synthesized inputs to
train a single-shot monocular pose estimation model. During training we
leverage differentiable rendering to supplement a state-of-the-art direct
regression objective with a novel smooth silhouette loss. Our results
demonstrate its qualitative and quantitative performance gains over traditional
silhouette objectives. Our data and code are available at
https://vcl3d.github.io/DronePoseComment: Accepted in ECCVW 202