论文标题
Diner：基于图像的深度感知神经辐射场
DINER: Depth-aware Image-based NEural Radiance fields
论文作者
论文摘要
我们提出了基于图像的深度感知神经辐射场（Diner）。考虑到一组稀疏的RGB输入视图，我们预测了深度和特征图，以指导体积场景表示的重建，该表示使我们能够在新颖的视图下呈现3D对象。具体而言，我们提出了新型技术，将深度信息纳入特征融合和有效的场景采样中。与先前的艺术状态相比，Diner可以达到更高的合成质量，并且可以以更大的差异处理输入视图。这使我们能够在不改变捕获硬件要求的情况下更全面地捕获场景，并最终在新颖的视图合成过程中实现更大的观点变化。我们通过综合人头和一般物体的新观点来评估我们的方法，并观察到与先前的最新状态相比，观察到明显改善的定性结果和增加的感知指标。该代码可公开用于研究目的。
We present Depth-aware Image-based NEural Radiance fields (DINER). Given a sparse set of RGB input views, we predict depth and feature maps to guide the reconstruction of a volumetric scene representation that allows us to render 3D objects under novel views. Specifically, we propose novel techniques to incorporate depth information into feature fusion and efficient scene sampling. In comparison to the previous state of the art, DINER achieves higher synthesis quality and can process input views with greater disparity. This allows us to capture scenes more completely without changing capturing hardware requirements and ultimately enables larger viewpoint changes during novel view synthesis. We evaluate our method by synthesizing novel views, both for human heads and for general objects, and observe significantly improved qualitative results and increased perceptual metrics compared to the previous state of the art. The code is publicly available for research purposes.
