Multi-view Stereo
MVS aims to reconstruct a 3D model from a collection of posed images, which can be further combined with traditional rendering algorithms to generate novel views.
Traditional methods explicitly establish pixel correspondences between images based on hand-crafted image features and then optimize the 3D structure to achieve the best pixel correspondences among images.
Learning-based MVS methods implicitly build multi-view correspondences with learnable features and regress depth or 3D volume based on the features in an end-to-end framework.
GaussianPro draws inspiration from depth optimization in MVS to improve the geometry of the Gaussians, thereby achieving better rendering results.
Neural Radiance Field
NeRF combines deep learning techniques with the 3D volumetric representation, transforming a 3D scene into a learnable continuous density field.
Utilizing ray m