Li, S. (2021). In-Place Panoptic Radiance Field Segmentation with Perceptual Prior for 3D Scene Understanding. JOURNAL OF LATEX CLASS FILES, 14(8).
This paper aims to address the limitations of existing 3D scene understanding methods by proposing a novel approach that integrates 2D panoptic segmentation with neural radiance fields, guided by perceptual priors, to achieve accurate and consistent 3D panoptic segmentation.
The proposed method utilizes a pre-trained 2D panoptic segmentation network to generate semantic and instance pseudo-labels for observed RGB images. These pseudo-labels, along with visual sensor pose information, are used to train an implicit scene representation and understanding model within a neural radiance field framework. The model consists of a multi-resolution voxel grid for geometric feature encoding and a separate understanding feature grid for semantic and instance encoding. Perceptual guidance from the pre-trained 2D segmentation network is incorporated to enhance the alignment between appearance, geometry, and panoptic understanding. Additionally, a segmentation consistency loss function and regularization terms based on patch-based ray sampling are introduced to improve the robustness and consistency of the learning process.
The proposed perceptual-prior-guided 3D scene representation and understanding method effectively addresses the limitations of existing methods by leveraging 2D panoptic segmentation information within a neural radiance field framework. The integration of perceptual priors, patch-based ray sampling, and a novel implicit scene representation model enables accurate and consistent 3D panoptic segmentation, advancing the field of 3D scene understanding.
This research significantly contributes to the field of 3D scene understanding by proposing a novel and effective method for achieving accurate and consistent 3D panoptic segmentation. The proposed approach has potential applications in various domains, including robotics, virtual reality, and autonomous driving, where accurate and comprehensive scene understanding is crucial.
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by Shenghao Li a las arxiv.org 10-08-2024
https://arxiv.org/pdf/2410.04529.pdfConsultas más profundas