Proceedings ArticleDOI
Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?
Aseem Behl,Omid Hosseini Jafari,Siva Karthik Mustikovela,Hassan Abu Alhaija,Carsten Rother,Andreas Geiger +5 more
- pp 2593-2602
TLDR
The importance of recognition granularity is investigated, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions, and it is observed that the instance segmentation cue is by far strongest, in the authors' setting.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.read more
Citations
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AI-Survey for Self-Flying Vehicles: Exploring the Challenges of Deep Learning
TL;DR: A survey for the topic of automated flights focusing on challenging Deep Learning problems with a state-of-the-art overview and an outline of possible sensor set-ups and AI based pipelines with leading results on established data sets are provided.
Journal ArticleDOI
Multi-task deep learning with optical flow features for self-driving cars
TL;DR: A new framework that exploits the use of a motion-based feature known as optical flow extracted from the dash camera is proposed, and it is demonstrated that such a feature is effective in significantly improving the accuracy of the control signals.
Journal ArticleDOI
A scene flow estimation method based on fusion segmentation and redistribution for autonomous driving
Proceedings ArticleDOI
Weakly- and Semi-Supervised Object Localization
YangQuan Chen,Mei-Chen Yeh +1 more
TL;DR: In this paper , a semi-supervised localization model is developed via self-training, where a small amount of data with full supervision is used to train a class-agnostic detector and use it to generate pseudo bounding boxes for data with weak supervision.
Journal ArticleDOI
Regularization for Unsupervised Learning of Optical Flow
TL;DR: In this paper , a shared-weight teacher-student strategy and a content-aware regularization (CAR) module are proposed to prevent motion estimation methods in unsupervised learning from co-adaptation.
References
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