SGE: Structured Light System Based on Gray Code with an Event Camera
Fast and accurate depth sensing has long been a significant research challenge. Event camera, as a device that quickly responds to intensity changes, provides a new solution for structured light (SL) systems. In this paper, we introduce Gray code into event-based SL systems for the first time. Our setup includes an event camera and a Digital Light Processing (DLP) projector, enabling depth estimation through high-speed projection and decoding of Gray code patterns. By employing Gray code for point matching in event-based SL system, our method is immune to timestamp noise, realizing high-speed depth estimation without loss of accuracy and spatial resolution. The binary nature of events and Gray code minimizes data redundancy, enabling us to fully utilize sensor bandwidth at 100%. Experimental results show that our approach achieves accuracy comparable to state-of-the-art scanning methods while surpassing them in data acquisition speed (up to 41 times improvement) without sacrificing accuracy and spatial resolution. Our proposed approach offers a highly promising solution for ultra-fast, real-time, and high-precision dense depth estimation.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning
This study focuses on event detection in optical fibers, specifically classifying six events using the Phase-OTDR system. A novel approach is introduced to enhance Phase-OTDR data analysis by transforming 1D data into gr…
Transfer LearningCross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements
This paper presents a cross-modal learning framework that exploits complementary information from depth and grayscale images for robust navigation. We introduce a Cross-Modal Wasserstein Autoencoder that learns shared la…
Reinforcement LearningGaze-Vector Estimation in the Dark with Temporally Encoded Event-driven Neural Networks
In this paper, we address the intricate challenge of gaze vector prediction, a pivotal task with applications ranging from human-computer interaction to driver monitoring systems. Our innovative approach is designed for …
Gaze PredictionFace Authentication from Grayscale Coded Light Field
Face verification is a fast-growing authentication tool for everyday systems, such as smartphones. While current 2D face recognition methods are very accurate, it has been suggested recently that one may wish to add a 3D…
Face RecognitionFace VerificationOne-Step Event-Driven High-Speed Autofocus
High-speed autofocus in extreme scenes remains a significant challenge. Traditional methods rely on repeated sampling around the focus position, resulting in ``focus hunting''. Event-driven methods have advanced focusing…