论文标题
神经密度距离场
Neural Density-Distance Fields
论文作者
论文摘要
现在，神经领域对3D视觉任务的成功是无可争议的。遵循这种趋势，已经提出了几种旨在进行视觉定位的方法（例如，大满贯）使用神经场估算距离或密度场。但是，很难仅通过基于密度字段的方法（例如神经辐射场（NERF））实现较高的定位性能，因为它们在大多数空地区不提供密度梯度。另一方面，基于距离场的方法，例如神经隐式表面（NEU）在物体表面形状中具有局限性。本文提出了神经密度距离场（NEDDF），这是一种新颖的3D表示，可相互约束距离和密度场。我们将距离场公式扩展到没有明确边界表面的形状，例如皮毛或烟雾，这可以从距离场到密度场的明确转换。通过显式转换实现的一致距离和密度字段使稳健性可以符合初始值和高质量的注册。此外，字段之间的一致性允许从稀疏点云中快速收敛。实验表明，NEDDF可以实现较高的本地化性能，同时在新型视图合成中提供了可比的结果。该代码可在https://github.com/ueda0319/neddf上找到。
The success of neural fields for 3D vision tasks is now indisputable. Following this trend, several methods aiming for visual localization (e.g., SLAM) have been proposed to estimate distance or density fields using neural fields. However, it is difficult to achieve high localization performance by only density fields-based methods such as Neural Radiance Field (NeRF) since they do not provide density gradient in most empty regions. On the other hand, distance field-based methods such as Neural Implicit Surface (NeuS) have limitations in objects' surface shapes. This paper proposes Neural Density-Distance Field (NeDDF), a novel 3D representation that reciprocally constrains the distance and density fields. We extend distance field formulation to shapes with no explicit boundary surface, such as fur or smoke, which enable explicit conversion from distance field to density field. Consistent distance and density fields realized by explicit conversion enable both robustness to initial values and high-quality registration. Furthermore, the consistency between fields allows fast convergence from sparse point clouds. Experiments show that NeDDF can achieve high localization performance while providing comparable results to NeRF on novel view synthesis. The code is available at https://github.com/ueda0319/neddf.
