论文标题
Viewnerf：使用类别级神经辐射场的无监督观点估计
ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields
论文作者
论文摘要
我们介绍了Viewnerf，这是一种基于神经辐射的观点估计方法，该方法学会了直接从训练过程中图像预测类别级别的观点。虽然Nerf通常接受接地摄像头姿势训练，但已经提出了多次扩展，以减少对这种昂贵的监督的需求。尽管如此，这些方法中的大多数仍在具有较大的摄像头动作的复杂设置中挣扎，并且仅限于单个场景，即不能在描绘相同对象类别的场景的集合中训练它们。为了解决这些问题，我们的方法使用合成方法分析，将条件的NERF与观点预测变量和场景编码器相结合，以便为整个对象类别产生自我监督的重建。我们不是专注于高保真重建，而是针对复杂方案中的有效和准确的观点预测，例如实际数据上的360°旋转。我们的模型在单场场景和多个现实集合中显示了合成和真实数据集的竞争结果。
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF is usually trained with ground-truth camera poses, multiple extensions have been proposed to reduce the need for this expensive supervision. Nonetheless, most of these methods still struggle in complex settings with large camera movements, and are restricted to single scenes, i.e. they cannot be trained on a collection of scenes depicting the same object category. To address these issues, our method uses an analysis by synthesis approach, combining a conditional NeRF with a viewpoint predictor and a scene encoder in order to produce self-supervised reconstructions for whole object categories. Rather than focusing on high fidelity reconstruction, we target efficient and accurate viewpoint prediction in complex scenarios, e.g. 360° rotation on real data. Our model shows competitive results on synthetic and real datasets, both for single scenes and multi-instance collections.
