Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks

Core Concepts

This paper provides a comprehensive survey of deep learning techniques for event-based vision, covering event representations, quality enhancement, image/video reconstruction and restoration, and scene understanding and 3D vision. It also conducts benchmark experiments and discusses challenges and future research directions in this field.

Abstract

The paper first examines the typical event representations and quality enhancement methods, as they play a crucial role as inputs to deep learning models. It then provides a comprehensive survey of existing deep learning-based methods, structurally grouping them into two major categories: 1) image/video reconstruction and restoration, and 2) event-based scene understanding and 3D vision. For image/video reconstruction and restoration, the paper reviews deep learning methods for event-based intensity reconstruction, event-guided image/video super-resolution, event-guided video frame interpolation, event-guided image/video deblurring, and event-based deep image/video HDR. It discusses the challenges in these tasks, such as scarce labeled data, high computational complexity, and low-quality reconstructed results, and highlights the key insights and advancements made by the representative methods. For event-based scene understanding and 3D vision, the paper covers deep learning techniques for semantic segmentation, feature tracking, object classification, object detection and tracking, 3D reconstruction, and visual SLAM. It analyzes the performance of these methods and identifies the critical problems that need to be addressed. The paper also conducts benchmark experiments for some representative research directions, such as image reconstruction, deblurring, and object recognition, to provide insights and identify the critical problems for future studies. Finally, the paper discusses the challenges and provides new perspectives to inspire more research studies in this field, such as event-based neural radiance for 3D reconstruction, cross-modal learning, and event-based model pretraining.

Stats

"Event cameras capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign) of the intensity changes." "Event cameras offer some other benefits, such as high temporal resolution and high dynamic range." "Deep learning has received great attention in this emerging area with amounts of techniques developed based on various purposes."

Quotes

"Event cameras have the potential to overcome the limitations of frame-based cameras in the computer vision and robotics community." "Previously, Gallego et al. [1] provided the first overview of the event-based vision with a particular focus on the principles and conventional algorithms. However, DL has invigorated almost every field of event-based vision recently, and remarkable advancements in methodologies and techniques have been achieved." "We survey the DL-based methods by focusing on three important aspects: 1) DNN input representations for event data and quality enhancement (Sec. 2); 2) Current research highlights by typically analyzing two hot fields, image restoration and enhancement (Sec.3), scene understanding and 3D vision (Sec. 4); 3) Potential future directions, such as event-based neural radiance for 3D reconstruction, cross-modal learning, and event-based model pertaining (Sec. 5.2)."

Key Insights Distilled From

Deep Learning for Event-based Vision

by Xu Zheng,Yex... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2302.08890.pdf

Deeper Inquiries

How can deep learning techniques be further improved to address the challenges of scarce labeled data and high computational complexity in event-based vision tasks?

In event-based vision tasks, deep learning techniques can be enhanced to tackle the challenges of scarce labeled data and high computational complexity through several strategies: Semi-supervised Learning: Leveraging semi-supervised learning approaches can help make the most of limited labeled data by incorporating unlabeled data into the training process. Techniques like self-training, consistency regularization, and pseudo-labeling can be employed to utilize the abundance of unlabeled data effectively. Transfer Learning: Transfer learning can be utilized to transfer knowledge from related tasks or domains with ample labeled data to the event-based vision tasks. By fine-tuning pre-trained models on event data, the need for a large amount of labeled data can be mitigated. Data Augmentation: Generating synthetic data through data augmentation techniques can help in expanding the training dataset and improving the model's generalization capabilities. Augmentation methods specific to event data, such as introducing noise patterns or simulating event streams, can be beneficial. Active Learning: Implementing active learning strategies can intelligently select the most informative samples for annotation, thereby optimizing the labeling process and maximizing the model's performance with minimal labeled data. Model Compression: To address high computational complexity, techniques like model distillation, pruning, and quantization can be applied to reduce the size and computational requirements of deep learning models while maintaining performance. By integrating these strategies into the development of deep learning models for event-based vision tasks, researchers can effectively overcome the challenges posed by limited labeled data and computational complexity.

How can event-based neural radiance models be leveraged for high-quality 3D reconstruction, and what are the key technical hurdles that need to be overcome?

Event-based neural radiance models offer a promising approach for high-quality 3D reconstruction by leveraging the unique properties of event data, such as high temporal resolution and dynamic range. To effectively utilize these models for 3D reconstruction, the following steps can be taken: Event Integration: Developing algorithms that can integrate event data with traditional frame-based data to create a comprehensive representation of the scene. This integration can provide richer information for 3D reconstruction tasks. Neural Radiance Fields (NeRF): Implementing neural radiance field models that can infer the 3D structure of a scene from event data. NeRF models can capture detailed geometry and appearance information, leading to high-quality reconstructions. Cross-Modal Learning: Exploring techniques that enable the fusion of event-based neural radiance models with other modalities, such as RGB images or depth sensors, to enhance the accuracy and completeness of 3D reconstructions. Key technical hurdles that need to be addressed in leveraging event-based neural radiance models for 3D reconstruction include: Sparse and Irregular Data: Event data is inherently sparse and asynchronous, posing challenges for traditional 3D reconstruction algorithms. Developing robust methods to handle this irregular data format is crucial. Complexity and Scalability: Neural radiance models can be computationally intensive, especially when dealing with large-scale 3D scenes. Optimizing these models for efficiency and scalability is essential for practical applications. Calibration and Synchronization: Ensuring accurate calibration and synchronization of event cameras with other sensors or modalities is critical for precise 3D reconstruction. Addressing issues related to sensor alignment and timing synchronization is vital. By overcoming these technical challenges and leveraging the strengths of event-based neural radiance models, high-quality 3D reconstructions with detailed geometry and appearance can be achieved in various applications, including robotics, augmented reality, and computer vision.