Abstract: The accelerated advancement of deep learning has notably propelled 3D reconstruction techniques within the field of computer vision.NeRFs have become an essential methodology due to their adeptness at scene modeling and superior view synthesis.However,challenges persist in dynamic environments,particularly in managing intricate lighting variations and transient object interference.Alterations in imaging conditions may lead to inconsistent scene appearances,thereby degrading the quality of view synthesis.Concurrently,dynamic elements can adversely affect the photorealism of reconstructed scenes.To mitigate these issues,this paper introduces an AS-NeRF.By incorporating frequency regularization and composite positional encoding into the sampling strategy,AS-NeRF enhances the efficiency of appearance feature fusion,thereby augmenting the model’s adaptability to variations in lighting and camera parameters.This subsequently improves color consistency and overall rendering realism.Additionally,a lightweight segmentation network is utilized to predict transient visibility masks in an end-to-end manner,effectively isolating dynamic objects and reducing their impact on view synthesis quality.The efficacy of AS-NeRF is verified through experiments conducted on the Photo Tourism datasets,which are compared qualitatively and quantitatively with several existingme-thods.The experimental results demonstrate that AS-NeRF surpasses existing approaches in terms of synthesis accuracy and further confirms the accuracy of the computed segmentation masks in distinguishing transient objects.

Key words: Neural radiance fields, View synthesis, Neural rendering, 3D reconstruction