36 citations · 38 across the 2 of their papers we have counts for
13 papers
AutoRF: Learning 3D Object Radiance Fields from Single View Observations
Norman Müller, Andrea Simonelli, Lorenzo Porzi +3
We introduce AutoRF - a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view. This setting is in stark…
Inferring Latent Domains for Unsupervised Deep Domain Adaptation
Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò +2
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available by leveraging information from annotated data…
Weakly Supervised Multi-Object Tracking and Segmentation
Idoia Ruiz, Lorenzo Porzi, Samuel Rota Bulò +2
We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do n…
Improving Panoptic Segmentation at All Scales
Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder
Crop-based training strategies decouple training resolution from GPU memory consumption, allowing the use of large-capacity panoptic segmentation networks on multi-megapixel images…
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?
Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi +2
Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark,…
Improving Optical Flow on a Pyramid Level
Markus Hofinger, Samuel Rota Bulò, Lorenzo Porzi +3
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…