用多时相卫星图生成带语义的3D场景，提升标签质量和颜色一致性。
Semantic Neural Radiance Fields for Multi-Date Satellite Data
- 基于多时相卫星图像与像素级标签，构建可生成3D语义场的NeRF模型
- 利用语义信息修复噪声标签，并解决车辆等动态物体导致的色彩不一致问题
- 开源了标注数据集与代码，适合遥感与三维重建研究者使用
本文提出一种面向卫星影像的神经辐射场（NeRF）模型，能够从一组带有像素级语义标签的多时相卫星图像中，生成场景的三维语义表示（神经语义场）。该模型展现出对噪声输入标签的鲁棒性，并能通过引入语义信息改善因非静止类别（如车辆）引起的时序图像不一致性问题，从而提升颜色预测精度。为推动该领域研究，我们构建了一个包含人工标注的多视角卫星图像数据集。相关代码与数据集已公开于 https://github.com/wagnva/semantic-nerf-for-satellite-data。
原文摘要 · Abstract (English)
In this work we propose a satellite specific Neural Radiance Fields (NeRF) model capable to obtain a three-dimensional semantic representation (neural semantic field) of the scene. The model derives the output from a set of multi-date satellite images with corresponding pixel-wise semantic labels. We demonstrate the robustness of our approach and its capability to improve noisy input labels. We enhance the color prediction by utilizing the semantic information to address temporal image inconsistencies caused by non-stationary categories such as vehicles. To facilitate further research in this domain, we present a dataset comprising manually generated labels for popular multi-view satellite images. Our code and dataset are available at https://github.com/wagnva/semantic-nerf-for-satellite-data.
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