112 citations · 114 across the 5 of their papers we have counts for
6 papers · 1 filter
SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion
Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch +3
This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring complex architectural redesigns. T…
Exploring Easy Boosts for Lidar Semantic Scene Completion
Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch +3
This paper investigates "free lunch" strategies to boost the performance of lidar semantic scene completion (SSC) without requiring complex architectural redesigns. We first demons…
LiDPM: Rethinking Point Diffusion for Lidar Scene Completion
Tetiana Martyniuk, Gilles Puy, Alexandre Boulch +2
Training diffusion models that work directly on lidar points at the scale of outdoor scenes is challenging due to the difficulty of generating fine-grained details from white noise…
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image
Tetiana Martyniuk, Orest Kupyn, Yana Kurliak +3
We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in the wild. It contains annotations of over 3.5K landmarks that acc…
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
Vasyl Borsuk, Roman Vei, Orest Kupyn +3
We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for ob…
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
Orest Kupyn, Tetiana Martyniuk, Junru Wu +1
We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring effic…