@inproceedings{
wang2024gsgnesf,
title={{GS}\${\textasciicircum}2\$-{GN}e{SF}: Geometry-Semantics Synergy for Generalizable Neural Semantic Fields},
author={Chengshun Wang and Na Zhao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=6bW35k0JhX}
}
The remarkable success of neural radiance fields in low-level vision tasks such as novel view synthesis has motivated its extension to high-level semantic understanding, giving rise to the concept of the neural semantic field (NeSF). NeSF aims to simultaneously synthesize novel view images and associated semantic segmentation maps. Generalizable NeSF, in particular, is an appealing direction as it can generalize to unseen scenes for synthesizing images and semantic maps for novel views, thereby avoiding the need for tedious per-scene optimization. However, existing approaches to generalizable NeSF fall short in fully exploiting the geometric and semantic features as well as their mutual interactions, resulting in suboptimal performance in both novel-view image synthesis and semantic segmentation. To address this limitation, we propose Geometry-Semantics Synergy for Generalized Neural Semantic Fields (GS$^2$-GNeSF), a novel approach aimed at improving the performance of generalizable NeSF through the comprehensive construction and synergistic interaction of geometric and semantic features. 
In GS$^2$-GNeSF, we introduce a robust geometric prior generator to generate the cost volumes and depth prior, which aid in constructing geometric features and facilitating geometry-aware sampling. Leveraging the depth prior, we additionally construct a global semantic context for the target view. This context provides two types of compensation information to enhance geometry and semantic features, achieved through boundary detection and semantic segmentation, respectively. Lastly, we present an efficient dual-directional interactive attention mechanism to foster deep interactions between the enhanced geometric and semantic features.
Experiments conducted on both synthetic and real datasets demonstrate that our GS$^2$-GNeSF outperforms existing methods in both novel view and semantic map synthesis, highlighting its effectiveness in generalizing neural semantic fields for unseen scenes.
GS^2-GNeSF: Geometry-Semantics Synergy for Generalizable Neural Semantic Fields
Chengshun Wang Na Zhao