Car-Studio：从单视图和无限野外图像学习汽车辐射场
Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images
AI总结：
针对自动驾驶模拟器中车辆旋转模糊问题，提出Car-Studio流程，从野外单视图图像学习车辆辐射场，实现清晰轮廓与可控外观编辑，性能优于基线。
摘要：
组合神经场景图研究表明，辐射场可以成为可编辑的自动驾驶模拟器中的高效工具。然而，先前的研究是在自动驾驶数据集的序列内进行学习，导致在模拟器中旋转汽车时出现令人不满意的模糊现象。在本信中，我们提出了一种学习无约束图像并从处理后的图像构建数据集的流程。为了满足模拟器的要求，即车辆在视角变化时保持清晰，且轮廓从背景中保持锐利以避免编辑时产生伪影，我们设计了车辆的辐射场，这是城市场景前景中的关键部分。通过实验，我们证明了我们的模型与基线相比取得了有竞争力的性能。利用从野外图像构建的数据集，我们的方法逐渐呈现出可控的外观编辑功能。我们将发布数据集和代码于 https://lty2226262.github.io/car-studio/ 以促进该领域的进一步研究。
英文摘要：
Compositional neural scene graph studies have shown that radiance fields can be an efficient tool in an editable autonomous driving simulator. However, previous studies learned within a sequence of autonomous driving datasets, resulting in unsatisfactory blurring when rotating the car in the simulator. In this letter, we propose a pipeline for learning unconstrained images and building a dataset from processed images. To meet the requirements of the simulator, which demands that the vehicle maintain clarity when the perspective changes and that the contour remains sharp from the background to avoid artifacts when editing, we design a radiation field of the vehicle, a crucial part of the urban scene foreground. Through experiments, we demonstrate that our model achieves competitive performance compared to baselines. Using the datasets built from in-the-wild images, our method gradually presents a controllable appearance editing function. We will release the dataset and code on https://lty2226262.github.io/car-studio/ to facilitate further research in the field.
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。
