NeRF-SOS: Any-View Self-supervised Object Segmentation from Complex Real-World Scenes

• URL: http://arxiv.org/abs/2209.08776v3
• Date: Thu, 22 Sep 2022 05:40:50 GMT
• Title: NeRF-SOS: Any-View Self-supervised Object Segmentation from Complex Real-World Scenes
• Authors: Zhiwen Fan, Peihao Wang, Yifan Jiang, Xinyu Gong, Dejia Xu, Zhangyang Wang
• Abstract summary: This paper carries out the exploration of self-supervised learning for object segmentation using NeRF for complex real-world scenes. Our framework, called NeRF with Self-supervised Object NeRF-SOS, encourages NeRF models to distill compact geometry-aware segmentation clusters. It consistently surpasses other 2D-based self-supervised baselines and predicts finer semantics masks than existing supervised counterparts.
• Score: 80.59831861186227
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural volumetric representations have shown the potential that Multi-layer Perceptrons (MLPs) can be optimized with multi-view calibrated images to represent scene geometry and appearance, without explicit 3D supervision. Object segmentation can enrich many downstream applications based on the learned radiance field. However, introducing hand-crafted segmentation to define regions of interest in a complex real-world scene is non-trivial and expensive as it acquires per view annotation. This paper carries out the exploration of self-supervised learning for object segmentation using NeRF for complex real-world scenes. Our framework, called NeRF with Self-supervised Object Segmentation NeRF-SOS, couples object segmentation and neural radiance field to segment objects in any view within a scene. By proposing a novel collaborative contrastive loss in both appearance and geometry levels, NeRF-SOS encourages NeRF models to distill compact geometry-aware segmentation clusters from their density fields and the self-supervised pre-trained 2D visual features. The self-supervised object segmentation framework can be applied to various NeRF models that both lead to photo-realistic rendering results and convincing segmentation maps for both indoor and outdoor scenarios. Extensive results on the LLFF, Tank & Temple, and BlendedMVS datasets validate the effectiveness of NeRF-SOS. It consistently surpasses other 2D-based self-supervised baselines and predicts finer semantics masks than existing supervised counterparts. Code is available at: https://github.com/VITA-Group/NeRF-SOS.

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