Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones

• URL: http://arxiv.org/abs/2602.21101v2
• Date: Thu, 26 Feb 2026 16:35:02 GMT
• Title: Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones
• Authors: Rong Zou, Marco Cannici, Davide Scaramuzza,
• Abstract summary: We present a unified framework that leverages asynchronous event streams alongside motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights.<n>Despite highly dynamic drone flights, where RGB frames are severely degraded by motion blur and pose priors become unreliable, our method reconstructs high-fidelity radiance fields and preserves fine scene details, delivering a performance gain of over 50% on real-world data.
• Score: 27.431207024401562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fast-flying aerial robots promise rapid inspection under limited battery constraints, with direct applications in infrastructure inspection, terrain exploration, and search and rescue. However, high speeds lead to severe motion blur in images and induce significant drift and noise in pose estimates, making dense 3D reconstruction with Neural Radiance Fields (NeRFs) particularly challenging due to their high sensitivity to such degradations. In this work, we present a unified framework that leverages asynchronous event streams alongside motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. By embedding event-image fusion into NeRF optimization and jointly refining event-based visual-inertial odometry priors using both event and frame modalities, our method recovers sharp radiance fields and accurate camera trajectories without ground-truth supervision. We validate our approach on both synthetic data and real-world sequences captured by a fast-flying drone. Despite highly dynamic drone flights, where RGB frames are severely degraded by motion blur and pose priors become unreliable, our method reconstructs high-fidelity radiance fields and preserves fine scene details, delivering a performance gain of over 50% on real-world data compared to state-of-the-art methods.

Related papers

• FRED: The Florence RGB-Event Drone Dataset [23.020669715621604]
  Small, fast, and lightweight drones present significant challenges for traditional RGB cameras due to their limitations in capturing fast-moving objects, especially under challenging lighting conditions.<n>Event cameras offer an ideal solution, providing high temporal definition and dynamic range, yet existing benchmarks often lack fine temporal resolution or drone-specific motion patterns, hindering progress in these areas.<n>This paper introduces the Florence RGB-Event Drone dataset (REDF), a novel multimodal dataset specifically designed for drone detection, tracking, and trajectory forecasting, combining RGB video and streams.
  arXiv  Detail & Related papers  (2025-06-05T15:40:41Z)
• SaENeRF: Suppressing Artifacts in Event-based Neural Radiance Fields [12.428456822446947]
  Event cameras offer advantages such as low latency, low power consumption, low bandwidth, and high dynamic range.<n>Reconstructing geometrically consistent and photometrically accurate 3D representations from event data remains fundamentally challenging.<n>We present SaENeRF, a novel self-supervised framework that effectively suppresses artifacts and enables 3D-consistent, dense radiance, and photorealistic NeRF reconstruction of static scenes solely from event streams.
  arXiv  Detail & Related papers  (2025-04-23T03:33:20Z)
• NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild [55.154625718222995]
  We introduce NeRF On-the-go, a simple yet effective approach that enables the robust synthesis of novel views in complex, in-the-wild scenes. Our method demonstrates a significant improvement over state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-05-29T02:53:40Z)
• AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height Large-scale Outdoor Scene Rendering [9.365775353436177]
  Existing neural radiance fields (NeRF)-based novel view synthesis methods for large-scale outdoor scenes are mainly built on a single altitude. We propose an end-to-end framework, termed AG-NeRF, and seek to reduce the training cost of building good reconstructions by synthesizing free-viewpoint images based on varying altitudes of scenes.
  arXiv  Detail & Related papers  (2024-04-18T04:54:28Z)
• Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion [67.15935067326662]
  Event cameras offer low power, low latency, high temporal resolution and high dynamic range. NeRF is seen as the leading candidate for efficient and effective scene representation. We propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras.
  arXiv  Detail & Related papers  (2023-09-15T17:52:08Z)
• Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Survey [11.176205645608865]
  We propose the Drone-NeRF framework to enhance the efficient reconstruction of large-scale drone photography scenes. Our approach involves dividing the scene into uniform sub-blocks based on camera position and depth visibility. Sub-scenes are trained in parallel using NeRF, then merged for a complete scene.
  arXiv  Detail & Related papers  (2023-08-30T03:17:57Z)
• ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering [83.75284107397003]
  We introduce ScatterNeRF, a neural rendering method which renders scenes and decomposes the fog-free background. We propose a disentangled representation for the scattering volume and the scene objects, and learn the scene reconstruction with physics-inspired losses. We validate our method by capturing multi-view In-the-Wild data and controlled captures in a large-scale fog chamber.
  arXiv  Detail & Related papers  (2023-05-03T13:24:06Z)
• Progressively Optimized Local Radiance Fields for Robust View Synthesis [76.55036080270347]
  We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window.
  arXiv  Detail & Related papers  (2023-03-24T04:03:55Z)
• E-NeRF: Neural Radiance Fields from a Moving Event Camera [83.91656576631031]
  Estimating neural radiance fields (NeRFs) from ideal images has been extensively studied in the computer vision community. We present E-NeRF, the first method which estimates a volumetric scene representation in the form of a NeRF from a fast-moving event camera.
  arXiv  Detail & Related papers  (2022-08-24T04:53:32Z)
• PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images [75.87721926918874]
  We present Progressively Deblurring Radiance Field (PDRF) PDRF is a novel approach to efficiently reconstruct high quality radiance fields from blurry images. We show that PDRF is 15X faster than previous State-of-The-Art scene reconstruction methods.
  arXiv  Detail & Related papers  (2022-08-17T03:42:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.