Канал: Event-based Robot Vision

Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation (ECCV 2024)

Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation (ECCV 2024)

CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Max (TRO)

CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Max (TRO)

Event based Background-Oriented Schlieren (TPAMI 2023) (5 min)

Event based Background-Oriented Schlieren (TPAMI 2023) (5 min)

Event Penguins: Low-power, Continuous Remote Behavioral Localization with Event Cameras (CVPR 2024)

Event Penguins: Low-power, Continuous Remote Behavioral Localization with Event Cameras (CVPR 2024)

Event based Background-Oriented Schlieren (TPAMI 2023)

Event based Background-Oriented Schlieren (TPAMI 2023)

A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework

A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework

イベントカメラを用いたオプティカルフロー推定：動きとは何か？ (ViEW 2022)

イベントカメラを用いたオプティカルフロー推定：動きとは何か？ (ViEW 2022)

Event-based Stereo Depth for SLAM in Autonomous Driving (BADUE Workshop at IROS 2022)

Event-based Stereo Depth for SLAM in Autonomous Driving (BADUE Workshop at IROS 2022)

Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion (AISY 2022)

Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion (AISY 2022)

Secrets of Event-Based Optical Flow (ECCV 2022, Oral)

Secrets of Event-Based Optical Flow (ECCV 2022, Oral)

Methods of Event Processing

Methods of Event Processing

Exercise 4: event integrator (ordinary or "direct" integrator)

Exercise 4: event integrator (ordinary or "direct" integrator)

Feature Detection and Tracking with a DAVIS

Feature Detection and Tracking with a DAVIS

Event-based Optical Flow: model-based methods

Event-based Optical Flow: model-based methods

Event cameras: Sampling in time vs in range

Event cameras: Sampling in time vs in range

Exercise 7: panoramic image reconstruction with a rotating event camera (i.e., Mosaicing)

Exercise 7: panoramic image reconstruction with a rotating event camera (i.e., Mosaicing)

Exercise 5: result of spatial convolutions on real event data (bicycle sequence)

Exercise 5: result of spatial convolutions on real event data (bicycle sequence)

Event-based cameras: Advantages, Disadvantages and Challenges

Event-based cameras: Advantages, Disadvantages and Challenges

Three main event camera designs

Three main event camera designs

Image Reconstruction - Discussion

Image Reconstruction - Discussion

Image Reconstruction - Timeline

Image Reconstruction - Timeline

Event cameras: High Dynamic Range (HDR)

Event cameras: High Dynamic Range (HDR)

Color event cameras

Color event cameras

Event-based Optical Flow: learning-based methods and comparison

Event-based Optical Flow: learning-based methods and comparison

Tracking blobs of events. Ingredients of the per-event processing paradigm

Tracking blobs of events. Ingredients of the per-event processing paradigm

Image Reconstruction Methods 2018 - 2020.05

Image Reconstruction Methods 2018 - 2020.05

Tracking motion-compensated event features

Tracking motion-compensated event features

Event Generation Models

Event Generation Models

Comparison of five event-based feature trackers

Comparison of five event-based feature trackers

Image Reconstruction from events - Direct Integration

Image Reconstruction from events - Direct Integration

Frame- vs Event-based cameras - the curious case of the spinning dot

Frame- vs Event-based cameras - the curious case of the spinning dot

Exercise 4: event integrator ("leaky" integrator)

Exercise 4: event integrator ("leaky" integrator)

Introduction to optical flow

Introduction to optical flow

Tracking user-defined shapes with point sets

Tracking user-defined shapes with point sets

Data Association and EKLT tracker

Data Association and EKLT tracker

Image Reconstruction Methods 2011-2014

Image Reconstruction Methods 2011-2014

Image Reconstruction Methods 2016-2017

Image Reconstruction Methods 2016-2017

How to acquire visual information?

How to acquire visual information?

Exercise 5: result of spatial convolutions on real event data (slider_depth sequence)

Exercise 5: result of spatial convolutions on real event data (slider_depth sequence)

Exercise 3: setting up the event visualizer (dvs_displayer)

Exercise 3: setting up the event visualizer (dvs_displayer)

Exercise 6: global optical flow estimation using contrast maximization on slider_far sequence

Exercise 6: global optical flow estimation using contrast maximization on slider_far sequence

Event-based Data Representation

Event-based Data Representation

Intro to the course Event-based Robot Vision (2020.04.21) at TU Berlin

Intro to the course Event-based Robot Vision (2020.04.21) at TU Berlin

Image reconstruction - Case Study BMVC 2014

Image reconstruction - Case Study BMVC 2014

Exercise 5: result of spatial convolutions on synthetic event data

Exercise 5: result of spatial convolutions on synthetic event data

Event camera companies and devices (2020.04.27)

Event camera companies and devices (2020.04.27)

Event cameras: Bio-inspired motivation - the visual pathways

Event cameras: Bio-inspired motivation - the visual pathways

Optical Flow: Frame-based vs Event-based

Optical Flow: Frame-based vs Event-based

Exercise 7: panoramic image reconstruction with a rotating event camera (Mosaicing)

Exercise 7: panoramic image reconstruction with a rotating event camera (Mosaicing)