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.
no code implementations • 9 Apr 2024 • Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
In this paper, we address the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a scene representation method achieving high-quality, photorealistic results for novel view synthesis.
no code implementations • 5 Apr 2024 • François Darmon, Lorenzo Porzi, Samuel Rota-Bulò, Peter Kontschieder
In this paper, we address common error sources for 3D Gaussian Splatting (3DGS) including blur, imperfect camera poses, and color inconsistencies, with the goal of improving its robustness for practical applications like reconstructions from handheld phone captures.
no code implementations • 5 Dec 2023 • Haithem Turki, Vasu Agrawal, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Deva Ramanan, Michael Zollhöfer, Christian Richardt
Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render.
no code implementations • 5 Nov 2023 • Linning Xu, Vasu Agrawal, William Laney, Tony Garcia, Aayush Bansal, Changil Kim, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Aljaž Božič, Dahua Lin, Michael Zollhöfer, Christian Richardt
We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields.
no code implementations • 9 Jun 2023 • Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Matthias Nießner
Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes.
no code implementations • CVPR 2023 • Paul-Edouard Sarlin, Daniel DeTone, Tsun-Yi Yang, Armen Avetisyan, Julian Straub, Tomasz Malisiewicz, Samuel Rota Bulo, Richard Newcombe, Peter Kontschieder, Vasileios Balntas
We bridge this gap by introducing OrienterNet, the first deep neural network that can localize an image with sub-meter accuracy using the same 2D semantic maps that humans use.
1 code implementation • CVPR 2023 • 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.
no code implementations • CVPR 2023 • 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.
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.
no code implementations • 1 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.
no code implementations • 3 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.
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.
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.
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.
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.
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.
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 +3
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.
Ranked #2 on Panoptic Segmentation on KITTI Panoptic Segmentation
2 code implementations • CVPR 2018 • Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Also, we demonstrate how frequently used checkpointing approaches can be made computationally as efficient as InPlace-ABN.
Ranked #3 on Semantic Segmentation on KITTI Semantic Segmentation
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.
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.
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.
1 code implementation • 3 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.
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.
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.
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.
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.