SaENeRF：抑制基于事件的神经辐射场中的伪影
SaENeRF: Suppressing Artifacts in Event-based Neural Radiance Fields
AI总结：
针对事件相机NeRF重建中的伪影问题，提出自监督框架SaENeRF，通过基于事件极性的归一化加速学习并引入正则化损失，显著提升重建质量。
摘要：
事件相机是一种神经形态视觉传感器，能够异步捕捉对数亮度变化，具有低延迟、低功耗、低带宽和高动态范围等显著优势。尽管这些特性使其成为高速场景的理想选择，但从事件数据中重建几何一致且光度准确的3D表示仍然面临根本性挑战。现有的基于事件的神经辐射场（NeRF）方法部分解决了这些挑战，但会因早期阶段的激进网络学习和事件相机固有的噪声而产生持续伪影。为了克服这些限制，我们提出了SaENeRF，一种新颖的自监督框架，能够有效抑制伪影，并仅从事件流中实现对静态场景的3D一致、密集且逼真的NeRF重建。我们的方法基于累积事件极性对预测辐射变化进行归一化，促进场景表示构建的渐进和快速学习。此外，我们引入了专门设计的正则化损失，用于抑制光度变化低于事件阈值的区域中的伪影，同时增强非零事件的光强度差异，从而提高重建场景的视觉保真度。大量的定性和定量实验表明，与现有方法相比，我们的方法显著减少了伪影，并实现了更优的重建质量。代码可在https://github.com/Mr-firework/SaENeRF获取。
英文摘要：
Event cameras are neuromorphic vision sensors that asynchronously capture changes in logarithmic brightness changes, offering significant advantages such as low latency, low power consumption, low bandwidth, and high dynamic range. While these characteristics make them ideal for high-speed scenarios, reconstructing geometrically consistent and photometrically accurate 3D representations from event data remains fundamentally challenging. Current event-based Neural Radiance Fields (NeRF) methods partially address these challenges but suffer from persistent artifacts caused by aggressive network learning in early stages and the inherent noise of event cameras. To overcome these limitations, we present SaENeRF, a novel self-supervised framework that effectively suppresses artifacts and enables 3D-consistent, dense, and photorealistic NeRF reconstruction of static scenes solely from event streams. Our approach normalizes predicted radiance variations based on accumulated event polarities, facilitating progressive and rapid learning for scene representation construction. Additionally, we introduce regularization losses specifically designed to suppress artifacts in regions where photometric changes fall below the event threshold and simultaneously enhance the light intensity difference of non-zero events, thereby improving the visual fidelity of the reconstructed scene. Extensive qualitative and quantitative experiments demonstrate that our method significantly reduces artifacts and achieves superior reconstruction quality compared to existing methods. The code is available at https://github.com/Mr-firework/SaENeRF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
