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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iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
TL;DR: This work presents the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem.
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Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes
TL;DR: In this article, a novel monocular 3D scene flow estimation method, called Mono-SF, is proposed to jointly estimate the 3D geometry and motion of the scene by combining multi-view geometry and single-view depth information.
Proceedings ArticleDOI
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions
TL;DR: This work pro-poses a novel data-driven approach for temporal fusion of scene flow estimates in a multi-frame setup to overcome the issue of occlusion, and provides a fast multi- frame extension for a variety of sceneflow estimators, which outperforms the underlying dual-frame approaches.
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L3DOC: Lifelong 3D Object Classification
Yuyang Liu,Yang Cong,Gan Sun +2 more
TL;DR: This is the first work about using lifelong learning to handle 3D object classification task without model fine-tuning or retraining, and the core idea of the proposed L3DOC model is to factorize PointNet in a perspective of lifelong learning, while capturing and storing the shared point-knowledge in a Perspective of layer-wise tensor factorization architecture.
Proceedings ArticleDOI
Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model
TL;DR: Zhang et al. as mentioned in this paper proposed a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model, which benefits from high-quality features and the structured modelling capability.
References
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Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
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