no code implementations • 11 Sep 2023 • Marcel Büsching, Josef Bengtson, David Nilsson, Mårten Björkman
We introduce FlowIBR, a novel approach for efficient monocular novel view synthesis of dynamic scenes.
no code implementations • 2 Jun 2023 • Josef Bengtson, David Nilsson, Che-Tsung Lin, Marcel Büsching, Fredrik Kahl
We present a generalizable novel view synthesis method which enables modifying the visual appearance of an observed scene so rendered views match a target weather or lighting condition without any scene specific training or access to reference views at the target condition.
no code implementations • 28 Dec 2021 • David Nilsson, Aleksis Pirinen, Erik Gärtner, Cristian Sminchisescu
As we study this task in a lifelong learning context, the agents should use knowledge gained in earlier visited environments in order to guide their exploration and active learning strategy in successively visited buildings.
no code implementations • 17 Dec 2020 • David Nilsson, Aleksis Pirinen, Erik Gärtner, Cristian Sminchisescu
We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding by actively selecting views for which to request annotation.
no code implementations • CVPR 2018 • David Nilsson, Cristian Sminchisescu
In this paper we present a deep, end-to-end trainable methodology to video segmentation that is capable of leveraging information present in unlabeled data in order to improve semantic estimates.
Ranked #7 on Video Semantic Segmentation on Cityscapes val