Towards Generalization Across Depth for Monocular 3D Object Detection

arXiv (technical report) /
By Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Elisa Ricci, Peter Kontschieder

Abstract

While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches lag much behind. This work advances the state of the art by introducing MoVi-3D, a novel, single-stage deep architecture for monocular 3D object detection. MoVi-3D builds upon a novel approach which leverages geometrical information to generate, both at training and test time, virtual views where the object appearance is normalized with respect to distance. These virtually generated views facilitate the detection task as they significantly reduce the visual appearance variability associated to objects placed at different distances from the camera. As a consequence, the deep model is relieved from learning depth-specific representations and its complexity can be significantly reduced. In particular, in this work we show that, thanks to our virtual views generation process, a lightweight, single-stage architecture suffices to set new state-of-the-art results on the popular KITTI3D benchmark.

More publications

Learning Multi-Object Tracking and Segmentation from Automatic Annotations

By Lorenzo Porzi, Markus Hofinger, Idoia Ruiz, Joan Serrat, Samuel Rota Bulò, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2020 /

Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition

By Frederik Warburg, Soren Hauberg, Manuel López-Antequera, Pau Gargallo, Yubin Kuang, Javier Civera
Conf. on Computer Vision and Pattern Recognition (CVPR) 2020 /

Modeling the Background for Incremental Learning in Semantic Segmentation

By Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo
Conf. on Computer Vision and Pattern Recognition (CVPR) 2020 /

The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale

By Christian Ertler, Jerneja Mislej, Tobias Ollmann, Lorenzo Porzi, Gerhard Neuhold, Yubin Kuang
arXiv (technical report) /

Disentangling Monocular 3D Object Detection

By Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, Peter Kontschieder
International Conf. on Computer Vision (ICCV) 2019 /

Seamless Scene Segmentation

By Lorenzo Porzi, Samuel Rota Bulò, Aleksander Colovic, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /

Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

By Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Samuel Rota Bulò, Nicu Sebe, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /

Deep Single Image Camera Calibration with Radial Distortion

By Manuel López-Antequera, Roger Marı́, Pau Gargallo, Yubin Kuang, Javier Gonzalez-Jimenez, Gloria Haro
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /

AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

By Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /

Boosting Domain Adaptation by Discovering Latent Domains

By Massimilano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018 /

Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View

By Albert Pumarola, Antonio Agudo, Lorenzo Porzi, Alberto Sanfeliu, Vincent Lepetit, Francesc Moreno-Noguer
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018 /

In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

By Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018 /

AutoDIAL: Automatic DomaIn Alignment Layers

By Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò
International Conf. on Computer Vision (ICCV) 2017 /

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

By Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulò, Peter Kontschieder
International Conf. on Computer Vision (ICCV) 2017 /

Loss Max-Pooling for Semantic Image Segmentation

By Samuel Rota Bulò, Gerhard Neuhold, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2017 /

Online Learning with Bayesian Classification Trees

By Samuel Rota Bulò, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2016 /

Dropout Distillation

By Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Intern. Conf. on Machine Learning (ICML) 2016 /