Search Results for author: Peter Kontschieder

Found 22 papers, 3 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.

Panoptic Lifting for 3D Scene Understanding with Neural Fields

no code implementations19 Dec 2022 Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Buló, Norman Müller, Matthias Nießner, Angela Dai, Peter Kontschieder

We propose Panoptic Lifting, a novel approach for learning panoptic 3D volumetric representations from images of in-the-wild scenes.

Panoptic Segmentation Scene Understanding

DiffRF: Rendering-Guided 3D Radiance Field Diffusion

no code implementations2 Dec 2022 Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Matthias Nießner

We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models.


AutoRF: Learning 3D Object Radiance Fields from Single View Observations

no code implementations CVPR 2022 Norman Müller, Andrea Simonelli, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, Peter Kontschieder

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.

Novel View Synthesis

Dense Prediction with Attentive Feature Aggregation

no code implementations1 Nov 2021 Yung-Hsu Yang, Thomas E. Huang, Min Sun, Samuel Rota Bulò, Peter Kontschieder, Fisher Yu

Our experiments show consistent and significant improvements on challenging semantic segmentation benchmarks, including Cityscapes, BDD100K, and Mapillary Vistas, at negligible computational and parameter overhead.

Boundary Detection BSDS500 +1

Weakly Supervised Multi-Object Tracking and Segmentation

no code implementations3 Jan 2021 Idoia Ruiz, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Joan Serrat

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 not provide any kind of mask annotation.

Instance Segmentation Multi-Object Tracking +4

Improving Panoptic Segmentation at All Scales

no code implementations CVPR 2021 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.

Panoptic Segmentation

Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?

no code implementations ICCV 2021 Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Elisa Ricci

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, in particular on the commonly reported validation split.

Monocular 3D Object Detection object-detection

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

Towards Generalization Across Depth for Monocular 3D Object Detection

no code implementations ECCV 2020 Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Elisa Ricci, Peter Kontschieder

While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches lag much behind.

Monocular 3D Object Detection object-detection

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

Disentangling Monocular 3D Object Detection

no code implementations ICCV 2019 Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, Peter Kontschieder

In this paper we propose an approach for monocular 3D object detection from a single RGB image, which leverages a novel disentangling transformation for 2D and 3D detection losses and a novel, self-supervised confidence score for 3D bounding boxes.

3D Object Detection From Monocular Images Disentanglement +2

Seamless Scene Segmentation

5 code implementations CVPR 2019 Lorenzo Porzi, Samuel Rota Bulò, Aleksander Colovic, Peter Kontschieder

In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results.

Panoptic Segmentation Scene Segmentation

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

no code implementations ICCV 2017 Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, Peter Kontschieder

The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25, 000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes.

Image Segmentation Instance Segmentation +2

Loss Max-Pooling for Semantic Image Segmentation

no code implementations CVPR 2017 Samuel Rota Bulò, Gerhard Neuhold, Peter Kontschieder

We introduce a novel loss max-pooling concept for handling imbalanced training data distributions, applicable as alternative loss layer in the context of deep neural networks for semantic image segmentation.

Image Segmentation Semantic Segmentation

Online Learning With Bayesian Classification Trees

no code implementations CVPR 2016 Samuel Rota Bulo, Peter Kontschieder

Randomized classification trees are among the most popular machine learning tools and found successful applications in many areas.

BIG-bench Machine Learning Classification +1

Decision Forests, Convolutional Networks and the Models in-Between

1 code implementation3 Mar 2016 Yani Ioannou, Duncan Robertson, Darko Zikic, Peter Kontschieder, Jamie Shotton, Matthew Brown, Antonio Criminisi

We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.

Image Classification Representation Learning

Deep Neural Decision Forests

no code implementations ICCV 2015 Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulo

We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner.

Representation Learning

Neural Decision Forests for Semantic Image Labelling

no code implementations CVPR 2014 Samuel Rota Bulo, Peter Kontschieder

In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees.

Representation Learning

GeoF: Geodesic Forests for Learning Coupled Predictors

no code implementations CVPR 2013 Peter Kontschieder, Pushmeet Kohli, Jamie Shotton, Antonio Criminisi

This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on.

Image Segmentation Semantic Segmentation +1

Context-Sensitive Decision Forests for Object Detection

no code implementations NeurIPS 2012 Peter Kontschieder, Samuel R. Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof

In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem.

General Classification object-detection +3

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