Search Results for author: Markus Hofinger

Found 4 papers, 0 papers with code

Mapillary Planet-Scale Depth Dataset

no code implementations ECCV 2020 Manuel López Antequera, Pau Gargallo, Markus Hofinger, Samuel Rota Bulò, Yubin Kuang, Peter Kontschieder

Learning-based methods produce remarkable results on single image depth tasks when trained on well-established benchmarks, however, there is a large gap from these benchmarks to real-world performance that is usually obscured by the common practice of fine-tuning on the target dataset.

Improving Optical Flow on a Pyramid Level

no code implementations ECCV 2020 Markus Hofinger, Samuel Rota Bulò, Lorenzo Porzi, Arno Knapitsch, Thomas Pock, Peter Kontschieder

In this work we review the coarse-to-fine spatial feature pyramid concept, which is used in state-of-the-art optical flow estimation networks to make exploration of the pixel flow search space computationally tractable and efficient.

Optical Flow Estimation

Learning Multi-Object Tracking and Segmentation from Automatic Annotations

no code implementations CVPR 2020 Lorenzo Porzi, Markus Hofinger, Idoia Ruiz, Joan Serrat, Samuel Rota Bulò, Peter Kontschieder

Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1. 9%/+7. 5% on cars/pedestrians), and MOTSNet improves by +4. 1% over previously best methods on the MOTSChallenge dataset.

Association Instance Segmentation +4

Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow

no code implementations13 Feb 2018 Markus Hofinger, Thomas Pock, Thomas Moosbrugger

Wood-composite materials are widely used today as they homogenize humidity related directional deformations.

Optical Flow Estimation

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