The invention discloses a
small sample new
view synthesis method based on reinitialization three-dimensional
Gaussian splashing. The method comprises the following steps: firstly, acquiring sparse three-dimensional
point cloud and camera internal and external parameters from a training view through a
motion recovery structure algorithm; then, sampling points are generated in the
point cloud bounding box by adopting a
spatial expansion hybrid sampling strategy, and a coarse-grained
Gaussian set is constructed and optimized; thirdly, obtaining a rendered image through
Gaussian initialization and splash rendering, calculating pixel importance through depth errors and transmissivity, generating a fine-grained Gaussian set through
back projection, and optimizing the fine-grained Gaussian set; and finally, calculating a sampling probability based on the cross-view contribution degree, screening key Gaussian distribution, and optimizing to form a final Gaussian set, thereby realizing high-quality new
view synthesis. According to the method, the problem of sparsity difference is solved by eliminating extended view dependence, the multi-view consistency and local geometric details of a new view scene are improved, and high-quality new
view synthesis of sparse data is realized.