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09/07/2023
SimNP: Learning Self-Similarity Priors Between Neural Points
Existing neural field representations for 3D object reconstruction eithe...
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06/05/2022
Recurrent Video Restoration Transformer with Guided Deformable Attention
Video restoration aims at restoring multiple high-quality frames from mu...
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02/28/2022
ERF: Explicit Radiance Field Reconstruction From Scratch
We propose a novel explicit dense 3D reconstruction approach that proces...
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12/23/2021
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
In the light of recent analyses on privacy-concerning scene revelation f...
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05/09/2021
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors
As autonomous driving and augmented reality evolve, a practical concern ...
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08/21/2020
Domain Adaptation of Learned Features for Visual Localization
We tackle the problem of visual localization under changing conditions, ...
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07/06/2020
TLIO: Tight Learned Inertial Odometry
In this work we propose a tightly-coupled Extended Kalman Filter framewo...
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03/24/2020
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Efficiently reconstructing complex and intricate surfaces at scale is a ...
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06/09/2019
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
Future prediction is a fundamental principle of intelligence that helps ...
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08/20/2018
FusionNet and AugmentedFlowNet: Selective Proxy Ground Truth for Training on Unlabeled Images
Recent work has shown that convolutional neural networks (CNNs) can be u...
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08/06/2018
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
Occlusions play an important role in disparity and optical flow estimati...
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02/20/2018
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks
Recent work has shown that optical flow estimation can be formulated as ...
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01/19/2018
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
The finding that very large networks can be trained efficiently and reli...
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07/03/2017
End-to-End Learning of Video Super-Resolution with Motion Compensation
Learning approaches have shown great success in the task of super-resolv...
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03/28/2017
Lucid Data Dreaming for Multiple Object Tracking
Convolutional networks reach top quality in pixel-level object tracking ...
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12/07/2016
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem. ...
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12/06/2016
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
The FlowNet demonstrated that optical flow estimation can be cast as a l...
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12/07/2015
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Recent work has shown that optical flow estimation can be formulated as ...
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04/26/2015