It is desirable to create 3D object models and 3D maps from 2D input images
for applications such as navigation, virtual tourism, and urban planning. The
traditional methods of creating 3D maps, (such as photogrammetry), require a
large number of images and odometry. Additionally, traditional methods have
difficulty with reflective surfaces and specular reflections; windows and
chrome in the scene can be problematic. Google Road View is a familiar
application, which uses traditional methods to fuse a collection of 2D input
images into the illusion of a 3D map. However, Google Road View does not create
an actual 3D object model, only a collection of views. The objective of this
work is to create an actual 3D object model using updated techniques. Neural
Radiance Fields (NeRF[1]) has emerged as a potential solution, offering the
capability to produce more precise and intricate 3D maps. Gaussian Splatting[4]
is another contemporary technique. This investigation compares Neural Radiance
Fields to Gaussian Splatting, and describes some of their inner workings. Our
primary contribution is a method for improving the results of the 3D
reconstructed models. Our results indicate that Gaussian Splatting was superior
to the NeRF technique.

使用更新的技术创建实际的三维物体模型，比较神经辐射场与高斯分层的内部工作，并提出改进三维重建模型结果的方法，结果显示高斯分层技术优于 NeRF 技术。