  Neural Radiance Field (NeRF) has been widely recognized for its excellence in
novel view synthesis and 3D scene reconstruction. However, their effectiveness
is inherently tied to the assumption of static scenes, rendering them
susceptible to undesirable artifacts when confronted with transient distractors
such as moving objects or shadows. In this work, we propose a novel paradigm,
namely "Heuristics-Guided Segmentation" (HuGS), which significantly enhances
the separation of static scenes from transient 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 meticulous 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 distractors for NeRFs trained
in non-static scenes. Project page: https://cnhaox.github.io/NeRF-HuGS/.
