  3D scene segmentation based on neural implicit representation has emerged
recently with the advantage of training only on 2D supervision. However,
existing approaches still requires expensive per-scene optimization that
prohibits generalization to novel scenes during inference. To circumvent this
problem, we introduce a generalizable 3D segmentation framework based on
implicit representation. Specifically, our framework takes in multi-view image
features and semantic maps as the inputs instead of only spatial information to
avoid overfitting to scene-specific geometric and semantic information. We
propose a novel soft voting mechanism to aggregate the 2D semantic information
from different views for each 3D point. In addition to the image features, view
difference information is also encoded in our framework to predict the voting
scores. Intuitively, this allows the semantic information from nearby views to
contribute more compared to distant ones. Furthermore, a visibility module is
also designed to detect and filter out detrimental information from occluded
views. Due to the generalizability of our proposed method, we can synthesize
semantic maps or conduct 3D semantic segmentation for novel scenes with solely
2D semantic supervision. Experimental results show that our approach achieves
comparable performance with scene-specific approaches. More importantly, our
approach can even outperform existing strong supervision-based approaches with
only 2D annotations. Our source code is available at:
https://github.com/HLinChen/GNeSF.
