Search Results for author:
Found 8 papers, 5 papers with code
Deep Event Visual Odometry
To remove the dependency on additional sensors and to push the limits of using only a single event camera, we present Deep Event VO (DEVO), the first monocular event-only system with strong performance on a large number of real-world benchmarks.
Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares
We propose a differentiable nonlinear least squares framework to account for uncertainty in relative pose estimation from feature correspondences.
Masked Event Modeling: Self-Supervised Pretraining for Event Cameras
The models pretrained with MEM are also label-efficient and generalize well to the dense task of semantic image segmentation.
E-NeRF: Neural Radiance Fields from a Moving Event Camera
We also show that combining events and frames can overcome failure cases of NeRF estimation in scenarios where only a few input views are available without requiring additional regularization.
Neural Implicit Representations for Physical Parameter Inference from a Single Video
Neural networks have recently been used to analyze diverse physical systems and to identify the underlying dynamics.
The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions
However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is highly dependent on accurate feature positions in the target frame.
Intrinsic Neural Fields: Learning Functions on Manifolds
Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative modeling.
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo
In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework.
