NeRF-HuGS: Improved Neural Radiance Fields in Non-static Scenes Using Heuristics-Guided Segmentation

Jiahao Chen, Yipeng Qin, Lingjie Liu, Jiangbo Lu, Guanbin Li

Abstract: Neural Radiance Field (NeRF) has been widely recognized for its excellence in novel view synthesis and 3D scene reconstruction. However, their effectiveness is in-herently tied to the assumption of static scenes, rendering them susceptible to undesirable artifacts when confronted with transient distractors such as moving objects or shad-ows. In this work, we propose a novel paradigm, namely “Heuristics-Guided Segmentation” (HuGS), which signifi-cantly enhances the separation of static scenes from tran-sient distractors by harmoniously combining the strengths of hand-crafted heuristics and state-of-the-art segmentation models, thus significantly transcending the limitations of previous solutions. Furthermore, we delve into the metic-ulous design of heuristics, introducing a seamless fusion of Structure-from-Motion (SfM)-based heuristics and color residual heuristics, catering to a diverse range of texture profiles. Extensive experiments demonstrate the superiority and robustness of our method in mitigating transient dis-tractors for NeRFs trained in non-static scenes. Project page: https://cnhaox.github.io/NeRF-HuGS/