6 accepted papers
The development of aerial holistic scene understanding algorithms is hindered by the scarcity of comprehensive datasets that enable both semantic and geometric reconstruction. While synthetic datasets offer an alternative, existing options exhibit task-specific limitations, unrealistic scene composi…
We present a novel self-distillation based self-supervised monocular depth estimation (SD-SSMDE) learning framework. In the first step, our network is trained in a self-supervised regime on high-resolution images with the photometric loss. The network is further used to generate pseudo depth labels…
Depth estimation plays a crucial role in robotic applications that require environment perception. With the introduction of convolutional neural networks, monocular depth estimation (MDE) methods have become viable alternatives to LiDAR and stereo reconstruction-based solutions. Such methods require…
In this paper we propose a novel boosting-based sliding window solution for object detection which can keep up with the precision of the state-of-the art deep learning approaches, while being 10 to 100 times faster. The solution takes advantage of multisensorial perception and exploits information f…
Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestr…
In this work we present an obstacle detection and tracking solution applied to Automated Guided Vehicles (AGVs) in industrial environments. The proposed method relies on information provided by an omnidirectional stereo vision system enabling 360 degree perception around the AGV. The stereo data is…