17 citations · 20 across the 5 of their papers we have counts for
8 papers
Self-supervised learning of object pose estimation using keypoint prediction
Zahra Gharaee, Felix Järemo Lawin, Per-Erik Forssén
This paper describes recent developments in object specific pose and shape prediction from single images. The main contribution is a new approach to camera pose prediction by self-…
Registration Loss Learning for Deep Probabilistic Point Set Registration
Felix Järemo Lawin, Per-Erik Forssén
Probabilistic methods for point set registration have interesting theoretical properties, such as linear complexity in the number of used points, and they easily generalize to join…
Learning What to Learn for Video Object Segmentation
Goutam Bhat, Felix Järemo Lawin, Martin Danelljan +4
Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined during inference with a given first-frame reference mask. The problem of ho…
Learning Fast and Robust Target Models for Video Object Segmentation
Andreas Robinson, Felix Järemo Lawin, Martin Danelljan +2
Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. The main difficulty is to effectivel…
Discriminative Online Learning for Fast Video Object Segmentation
Andreas Robinson, Felix Järemo Lawin, Martin Danelljan +2
We address the highly challenging problem of video object segmentation. Given only the initial mask, the task is to segment the target in the subsequent frames. In order to effecti…
Density Adaptive Point Set Registration
Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan +2
Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clou…
Deep Projective 3D Semantic Segmentation
Felix Järemo Lawin, Martin Danelljan, Patrik Tosteberg +3
Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmenta…
Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation
Felix Järemo Lawin, Per-Erik Forssén, Hannes Ovrén
In this paper we introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging. The algorithm generates multiple depth hypotheses and uses a sp…