RSINS‐GS: Reconstruction From Single Image With Noise‐Added Strategy and 3D‐GS

ABSTRACT Multi‐input reconstruction methods such as 3D‐GS and NeRF excel in fidelity, yet they impose stringent requirements on the sequentiality of the input images. In contrast, single‐view reconstruction methods are designed to extract certain features of the image even under limited input conditions. However, the majority of current single‐view methods demand considerable graphics card performance for rendering at high resolutions and attaining high‐fidelity image reconstruction at lower resolutions remains a formidable challenge. To enhance the fidelity of reconstructed images while considering the constraints of graphics card performances, we propose a novel pipeline based on novel‐view synthetic (NVS), super‐resolution (SR) and 3D‐GS, named RSINS‐GS. First, we introduce a divide‐and‐conquer strategy tailored to reap pixel‐reinforced novel sequential views to render the reconstruction result without overburdening the graphics card, maintaining optimal performance and visual fidelity. Furthermore, to enhance the high fidelity of reconstructed images both in terms of qualitative and quantitative measures, we integrate 2D prior images with their corresponding geometric structural complements. Additionally, we introduce an innovative, generalised noise‐added strategy to refine the overall reconstruction process. Extensive experimental evaluations on Nerf_synthetic datasets and Google scanned datasets show that our method achieves high quality results.