Core Concepts
Hierarchical supervision with N2F2 enhances 3D scene understanding through multi-scale feature fields.
Abstract
Introduction:
Complex scenes require hierarchical understanding.
Radiance fields like NeRF advance 3D scene understanding.
Nested Neural Feature Fields (N2F2):
Introduces hierarchical supervision for a unified feature field.
Utilizes CLIP embeddings and deferred rendering for multi-scale representations.
Experiments:
Outperforms LERF and LangSplat in open-vocabulary 3D segmentation and localization tasks.
Composite embedding strategy improves efficiency during querying.
Ablation Studies:
Composite embedding method surpasses explicit scale selection, offering speed advantages.
Limitations:
Struggles with global context queries but excels in compound object descriptions.
Conclusion:
N2F2 significantly advances hierarchical scene understanding with superior performance.
Stats
Hierarchical supervisionを使用して、複数の粒度でシーンの特性をエンコードする単一の特徴フィールドを学習します。
提案された階層的監督方法は、CLIP埋め込みと遅延レンダリングを利用して、マルチスケール表現を実現します。