针对水下场景优化的NeRF模型，有效去除浮动物干扰
AquaNeRF: Neural Radiance Fields in Underwater Media with Distractor Removal
- 每条光线估计单一表面，抑制漂浮物和运动物体干扰
- 在PSNR上比Nerfacto提升7.5%，比SeaThru-NeRF高6.2%
- 适合海洋生物成像与水下静态场景重建任务
神经辐射场（NeRF）研究在野外静态视频建模方面取得显著进展，但现有模型极少考虑水下场景，而此类场景对海洋生物研究与拍摄具有重要意义。它们无法处理水下特有的视觉伪影，如游动的鱼和悬浮颗粒。本文提出一种新型基于MLP的NeRF渲染器与优化方案，通过为每条光线估计单一表面，降低浮动物和移动物体对感兴趣静态目标的干扰。采用带小偏移的高斯权重函数，确保周围介质透射率恒定。此外，引入基于深度的梯度缩放函数，增强近相机区域的梯度响应。实验表明，该方法在PSNR上相比基准模型Nerfacto提升约7.5%，比SeaThru-NeRF高6.2%。主观评估显示，相比现有方法，伪影显著减少，同时保留了静态目标与背景的细节。
原文摘要 · Abstract (English)
Neural radiance field (NeRF) research has made significant progress in modeling static video content captured in the wild. However, current models and rendering processes rarely consider scenes captured underwater, which are useful for studying and filming ocean life. They fail to address visual artifacts unique to underwater scenes, such as moving fish and suspended particles. This paper introduces a novel NeRF renderer and optimization scheme for an implicit MLP-based NeRF model. Our renderer reduces the influence of floaters and moving objects that interfere with static objects of interest by estimating a single surface per ray. We use a Gaussian weight function with a small offset to ensure that the transmittance of the surrounding media remains constant. Additionally, we enhance our model with a depth-based scaling function to upscale gradients for near-camera volumes. Overall, our method outperforms the baseline Nerfacto by approximately 7.5\% and SeaThru-NeRF by 6.2% in terms of PSNR. Subjective evaluation also shows a significant reduction of artifacts while preserving details of static targets and background compared to the state of the arts.
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