A Survey on Event-driven 3D Reconstruction: Development under Different Categories
It provides a comprehensive overview for researchers in computer vision, but it is incremental as a survey paper.
This survey reviews event-driven 3D reconstruction methods, categorizing them by approach and covering emerging trends like neural radiance fields, to highlight innovations and identify research gaps for future work.
Event cameras have gained increasing attention for 3D reconstruction due to their high temporal resolution, low latency, and high dynamic range. They capture per-pixel brightness changes asynchronously, allowing accurate reconstruction under fast motion and challenging lighting conditions. In this survey, we provide a comprehensive review of event-driven 3D reconstruction methods, including stereo, monocular, and multimodal systems. We further categorize recent developments based on geometric, learning-based, and hybrid approaches. Emerging trends, such as neural radiance fields and 3D Gaussian splatting with event data, are also covered. The related works are structured chronologically to illustrate the innovations and progression within the field. To support future research, we also highlight key research gaps and future research directions in dataset, experiment, evaluation, event representation, etc.