Search Results for author: Manasi Muglikar

Found 10 papers, 3 papers with code

Seeing Behind Dynamic Occlusions with Event Cameras

no code implementations28 Jul 2023 Rong Zou, Manasi Muglikar, Nico Messikommer, Davide Scaramuzza

We present the first large-scale dataset consisting of synchronized images and event sequences to evaluate our approach.

Image Inpainting

Event-based Shape from Polarization

no code implementations CVPR 2023 Manasi Muglikar, Leonard Bauersfeld, Diederik Paul Moeys, Davide Scaramuzza

Our method uses the continuous event stream caused by the rotation to reconstruct relative intensities at multiple polarizer angles.

Cracking Double-Blind Review: Authorship Attribution with Deep Learning

1 code implementation14 Nov 2022 Leonard Bauersfeld, Angel Romero, Manasi Muglikar, Davide Scaramuzza

In this work, we present a transformer-based, neural-network architecture that only uses the text content and the author names in the bibliography to attribute an anonymous manuscript to an author.

Attribute Authorship Attribution

Dense Continuous-Time Optical Flow from Events and Frames

1 code implementation25 Mar 2022 Mathias Gehrig, Manasi Muglikar, Davide Scaramuzza

To the best of our knowledge, our model is the first method that can regress dense pixel trajectories from event data.

Optical Flow Estimation

ESL: Event-based Structured Light

no code implementations30 Nov 2021 Manasi Muglikar, Guillermo Gallego, Davide Scaramuzza

Event cameras are bio-inspired sensors providing significant advantages over standard cameras such as low latency, high temporal resolution, and high dynamic range.

3D Reconstruction

Event Guided Depth Sensing

no code implementations20 Oct 2021 Manasi Muglikar, Diederik Paul Moeys, Davide Scaramuzza

The depth estimation is achieved by an event-based structured light system consisting of a laser point projector coupled with a second event-based sensor tuned to detect the reflection of the laser from the scene.

Autonomous Driving Depth Estimation

AlphaPilot: Autonomous Drone Racing

no code implementations26 May 2020 Philipp Foehn, Dario Brescianini, Elia Kaufmann, Titus Cieslewski, Mathias Gehrig, Manasi Muglikar, Davide Scaramuzza

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning.

Navigate Trajectory Planning

Voxel Map for Visual SLAM

no code implementations4 Mar 2020 Manasi Muglikar, Zichao Zhang, Davide Scaramuzza

We propose a voxel-map representation to efficiently retrieve map points for visual SLAM.

Redesigning SLAM for Arbitrary Multi-Camera Systems

no code implementations4 Mar 2020 Juichung Kuo, Manasi Muglikar, Zichao Zhang, Davide Scaramuzza

We adapt a state-of-the-art visual-inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e. g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.

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