Mazur 2022 - Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding 논문 리뷰
Abstract
Introduction
3D scene 속 실시간 semantic segmentation을 하는 방법을 제안합니다. Pre-trained 2D image feature extractor (여기서는 EfficientNet과 DINO)와 iMAP과 비슷한 SLAM 백엔드 시스템을 이용해서 정보를 퓨전시킵니다. 퓨전을 하는 방법은 latent volumetric rendering 테크닉을 사용합니다. 이 방법을 사용함으로써 EfficientNet 및 DINO의 feature-map을 퓨전해서 3D 공간에 고정시킬 수 있습니다.
The system is particularly effective for dynamic open set semantic segmentation. In this context, dynamic open set semantic segmentation refers to the ability to identify and group objects of varying appearance and geometry, given a semantic class, with minimal user input. This is a challenging problem in the field of computer vision and the authors’ approach presents a promising solution.
The introduction also provides a brief overview of related work in the field. This includes SemanticFusion, Semantic NeRF, Distilled Feature Fields (DFF), and Neural Feature Fusion Fields (N3F). Each of these methods has its own strengths and weaknesses, but the authors argue that their proposed method offers several advantages over these existing approaches.
In particular, the authors highlight that their method is capable of handling dynamic scenes, which is a significant challenge in the field of semantic segmentation. Furthermore, their method is designed to be efficient, which is a critical requirement for real-time applications.
Overall, the introduction sets the stage for the rest of the paper, which delves into the details of the proposed method, provides experimental results demonstrating its effectiveness, and discusses potential future directions for this line of research.