Biography
I am a Postdoctoral Researcher at the Robotics and Perception Group, led by Prof. Davide Scaramuzza. My current research centers on advancing visual perception with event-based cameras. Throughout my career, I have contributed to advancements in the areas of neural radiance fields reconstruction under fast motion, low-power perception with efficient neural networks and neuromorphic hardware, and monocular vision for autonomous vehicles. In the past, I’ve also been interested in integrating differentiable algorithms into end-to-end trainable models, with a focus on enhancing their robustness and explainability.
Interests
- Deep Learning
- Computer Vision
- Event-Based Cameras
- Representation Learning
- Implicit Differentiation
Education
-
PhD Student in Information Technology, 2022
Politecnico di Milano
-
MSc in Computer Science and Engineering, 2018
Politecnico di Milano
-
BSc in Computer Science and Engineering, 2015
Politecnico di Milano
Publications
Quickly discover relevant content by filtering publications.
(2025).
GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control.
arXiv preprint arXiv:2412.11198.
(2024).
Deep Visual Odometry with Events and Frames.
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(2024).
FARSE-CNN: Fully Asynchronous, Recurrent and Sparse Event-Based CNN.
European Conference on Computer Vision.
(2024).
FaVoR: Features via Voxel Rendering for Camera Relocalization.
arXiv preprint arXiv:2409.07571.
(2024).
Low-power event-based face detection with asynchronous neuromorphic hardware.
2024 International Joint Conference on Neural Networks (IJCNN).
(2024).
Mitigating motion blur in neural radiance fields with events and frames.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
(2024).
Revisiting token pruning for object detection and instance segmentation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.
(2024).
Monocular Event-Based Vision for Obstacle Avoidance with a Quadrotor.
arXiv preprint arXiv:2411.03303.
(2023).
A 5-point minimal solver for event camera relative motion estimation.
Proceedings of the IEEE/CVF International Conference on Computer Vision.
(2023).
Deep learning for asteroids autonomous terrain relative navigation.
Advances in Space Research.
(2023).
NeuralPUMA: Learning to Phase Unwrap Through Differentiable Graph Cuts.
IEEE Transactions on Signal Processing.
(2023).
SoftCut: A Fully Differentiable Relaxed Graph Cut Approach for Deep Learning Image Segmentation.
International Conference on Machine Learning, Optimization, and Data Science.
(2022).
E2 (go) motion: Motion augmented event stream for egocentric action recognition.
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.
(2022).
6 DoF Pose Regression via Differentiable Rendering.
International Conference on Image Analysis and Processing.
(2021).
DA4Event: Towards Bridging the Sim-to-Real Gap for Event Cameras Using Domain Adaptation.
IEEE Robotics and Automation Letters.
(2021).
Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*.
International Conference on Machine Learning, Optimization, and Data Science.
(2021).
Skeleton-based action recognition via spatial and temporal transformer networks.
Computer Vision and Image Understanding.
(2021).
N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain Adaptation.
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
(2021).
Neural weighted a*: Learning graph costs and heuristics with differentiable anytime a.
International Conference on Machine Learning, Optimization, and Data Science.
(2021).
Spatial Temporal Transformer Network for Skeleton-Based Action Recognition.
Pattern Recognition. ICPR International Workshops and Challenges.
(2020).
A Differentiable Recurrent Surface for Asynchronous Event-Based Data.
Computer Vision – ECCV 2020.
(2020).
A Differentiable Recurrent Surface for Asynchronous Event-Based Data.
Computer Vision – ECCV 2020.
(2019).
Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
(2019).
Attention Mechanisms for Object Recognition With Event-Based Cameras.
2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
Teaching
*
Contact
Marco Cannici
Robotics and Perception Group
Andreasstrasse 15, Zurich, 8050