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Many autonomous systems forecast aspects of the future in order to aid decision-making.
The Hungarian Method for the Assignment Problem
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A Hierarchical Representation for Future Action Prediction
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Anticipating Human Activities Using Object Affordances for Reactive Robotic Response
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Social LSTM: Human Trajectory Prediction in Crowded Spaces
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SE3-Nets: Learning Rigid Body Motion Using Deep Neural Networks
A. Byravan and D. Fox · 2017
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SE3-Nets: Learning Rigid Body Motion Using Deep Neural Networks
A. Byravan and D. Fox · 2017
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Geometry-Based Next Frame Prediction from Monocular Video
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A Point Set Generation Network for 3D Object Reconstruction from a Single Image
H. Fan, H. Su, and L. Guibas · 2017
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Unsupervised Learning of Depth and Ego-Motion from Video
T. Zhou, M. Brown, G. Noah, S. Google, and D. G. Lowe Google · 2017
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Deep Reinforcement Learning for Autonomous Driving
S. Wang, D. Jia, and X. Weng · 2018
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SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control
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R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting
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A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi · 2018
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Convolutional Social Pooling for Vehicle Trajectory Prediction
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Structure Preserving Video Prediction
X. Y. Jingwei Xu, Bingbing Ni, Zefan Li, Shuo Cheng · 2018
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Folded Recurrent Neural Networks for Future Video Prediction
M. Oliu, J. Selva, and S. Escalera · 2018
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3D Motion Decomposition for RGBD Future Dynamic Scene Synthesis
X. Qi, Z. Liu, D. corporation Qifeng Chen, J. Jia CUHK, and Y. Lab · 2019
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PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
G. Yang, X. Huang, Z. Hao, M.-Y. Liu, S. Belongie, and B. Hariharan · 2019
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3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
D. W. Shu, S. W. Park, and J. Kwon · 2019
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Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image
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DSCnet: Replicating LiDAR Point Clouds with Deep Sensor Cloning
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Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
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Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
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Rotational Rectification Network: Enabling Pedestrian Detection for Mobile Vision
X. Weng, S. Wu, F. Beainy, and K. Kitani · 2018
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Deep Continuous Fusion for Multi-Sensor 3D Object Detection
M. Liang, B. Yang, S. Wang, and R. Urtasun · 2018
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End-to-End Interpretable Neural Motion Planner
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Future Near-Collision Prediction from Monocular Video: Feasibility, Dataset, and Challenges
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P. Tomasello, S. Sidhu, A. Shen, M. W. Moskewicz, N. Redmon, G. Joshi, R. Phadte, P. Jain, and F. N. Iandola · 2019
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Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
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PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
S. Shi, X. Wang, and H. Li · 2019
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Deep Closest Point: Learning Representations for Point Cloud Registration
Y. Wang and J. M. Solomon · 2019
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FlowNet3D: Learning Scene Flow in 3D Point Clouds
X. Liu, C. R. Qi, and L. J. Guibas · 2019
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TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions
R. Chandra, U. Bhattacharya, A. Bera, and D. Manocha · 2019
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PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
M. Liang, B. Yang, W. Zeng, Y. Chen, R. Hu, S. Casas, and R. Urtasun · 2020
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nuScenes: A Multimodal Dataset for Autonomous Driving
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, and Q. Xu · 2020
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Joint 3D Tracking and Forecasting with Graph Neural Network and Diversity Sampling
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Pnpnet: End-to-end perception and prediction with tracking in the loop
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Joint Detection and Multi-Object Tracking with Graph Neural Networks
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