Aseem Behl

Ph.D. Student

Autonomous Vision

Office: S2.017
Max-Planck-Ring 4
72076 Tübingen
Germany

My research is in the area of Computer Vision and Machine Learning. Currently, I am working on problems related to estimation of 3D motion fields from stereo image sequences.

Website: http://aseembehl.github.io/

Google Scholar: https://scholar.google.de/citations?user=UEivs9sAAAAJ&hl=en

computer vision machine learning

Global Localization and Affordance Learning

Intelligent systems not only require the relative motion of objects around them \cite{Liu2017IROS}\cite{Liu2018IROS}, but typically also a precise global location \cite{Camposeco2017ICCV} with respect to a map, i.e. for planning or navigation tasks. With LOST \cite{Brubaker2016PAMI}, we have demonstrated that localization solel...

Andreas Geiger Joel Janai Fatma Güney Aseem Behl Laura Sevilla Yiyi Liao Benjamin Coors Moritz Menze Jun Xie

Object Scene Flow

To recover 3D motion \cite{Menze2018JPRS}, we have developed a state-of-the-art 3D scene flow estimation technique which exploits recognition (bounding boxes, instance segmentation and object coordinates) to support the challenging matching task \cite{Behl2017ICCV}.

While great progress has been made in recent years, large...

Moritz Menze Aseem Behl Andreas Geiger

6 results (View BibTeX file of all listed publications)

2019

PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds

Behl, A., Paschalidou, D., Donne, S., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Abstract

Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.

pdf suppmat Project Page Poster Video [BibTex]

2019

Behl, A., Paschalidou, D., Donne, S., Geiger, A. PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

pdf suppmat Project Page Poster Video [BibTex]

2017

Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?

Behl, A., Jafari, O. H., Mustikovela, S. K., Alhaija, H. A., Rother, C., Geiger, A.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)

Abstract

Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.

pdf suppmat Poster Project Page [BibTex]

2017

Behl, A., Jafari, O. H., Mustikovela, S. K., Alhaija, H. A., Rother, C., Geiger, A. Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)

pdf suppmat Poster Project Page [BibTex]

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, J., Güney, F., Behl, A., Geiger, A.

Arxiv, 2017 (article)

Abstract

Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. As with any rapidly growing field, however, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several topic specific survey papers have been written, to date no general survey on problems, datasets and methods in computer vision for autonomous vehicles exists. This paper attempts to narrow this gap by providing a state-of-the-art survey on this topic. Our survey includes both the historically most relevant literature as well as the current state-of-the-art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding and end-to-end learning. Towards this goal, we first provide a taxonomy to classify each approach and then analyze the performance of the state-of-the-art on several challenging benchmarking datasets including KITTI, ISPRS, MOT and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we will also provide an interactive platform which allows to navigate topics and methods, and provides additional information and project links for each paper.

pdf Project Page Project Page [BibTex]

Janai, J., Güney, F., Behl, A., Geiger, A. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art Arxiv, 2017 (article)

pdf Project Page Project Page [BibTex]

2015

Optimizing Average Precision using Weakly Supervised Data

Behl, A., Mohapatra, P., Jawahar, C. V., Kumar, M. P.

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2015 (article)

[BibTex]

2015

Behl, A., Mohapatra, P., Jawahar, C. V., Kumar, M. P. Optimizing Average Precision using Weakly Supervised Data IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2015 (article)

[BibTex]

2014

Optimizing Average Precision using Weakly Supervised Data

Behl, A., Jawahar, C. V., Kumar, M. P.

IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2014, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2014 (conference)

[BibTex]

2014

Behl, A., Jawahar, C. V., Kumar, M. P. Optimizing Average Precision using Weakly Supervised Data IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2014, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2014 (conference)

[BibTex]

Learning to Rank using High-Order Information

Dokania, P. K., Behl, A., Jawahar, C. V., Kumar, M. P.

International Conference on Computer Vision, 2014 (conference)

[BibTex]

Dokania, P. K., Behl, A., Jawahar, C. V., Kumar, M. P. Learning to Rank using High-Order Information International Conference on Computer Vision, 2014 (conference)

[BibTex]

Aseem Behl

Global Localization and Affordance Learning

Object Scene Flow

2019

PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds

2019

2017

Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?

2017

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

2015

Optimizing Average Precision using Weakly Supervised Data

2015

2014

Optimizing Average Precision using Weakly Supervised Data

2014

Learning to Rank using High-Order Information

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