SegNeRF: 3D Part Segmentation with Neural Radiance Fields (2211.11215v2)

Abstract: Recent advances in Neural Radiance Fields (NeRF) boast impressive performances for generative tasks such as novel view synthesis and 3D reconstruction. Methods based on neural radiance fields are able to represent the 3D world implicitly by relying exclusively on posed images. Yet, they have seldom been explored in the realm of discriminative tasks such as 3D part segmentation. In this work, we attempt to bridge that gap by proposing SegNeRF: a neural field representation that integrates a semantic field along with the usual radiance field. SegNeRF inherits from previous works the ability to perform novel view synthesis and 3D reconstruction, and enables 3D part segmentation from a few images. Our extensive experiments on PartNet show that SegNeRF is capable of simultaneously predicting geometry, appearance, and semantic information from posed images, even for unseen objects. The predicted semantic fields allow SegNeRF to achieve an average mIoU of $\textbf{30.30%}$ for 2D novel view segmentation, and $\textbf{37.46%}$ for 3D part segmentation, boasting competitive performance against point-based methods by using only a few posed images. Additionally, SegNeRF is able to generate an explicit 3D model from a single image of an object taken in the wild, with its corresponding part segmentation.

• The paper introduces a dual-field approach that jointly models geometry, appearance, and semantic parts for 3D segmentation.
• It achieves competitive mIoU scores of 37.46% for 3D segmentation and 30.30% for 2D segmentation on the PartNet dataset.
• The method requires minimal data, generalizes to unseen objects, and can reconstruct detailed 3D models from a single image.

"SegNeRF: 3D Part Segmentation with Neural Radiance Fields" explores the application of Neural Radiance Fields (NeRF) in discriminative tasks, centering on 3D part segmentation. While NeRF has shown remarkable results in generative tasks such as novel view synthesis and 3D reconstruction by relying solely on posed images, its potential for 3D part segmentation has not been extensively studied. This paper seeks to fill that gap by introducing SegNeRF.

Key Contributions:

1. Neural Field Representation:
   • SegNeRF builds upon traditional NeRF by integrating a semantic field alongside the standard radiance field.
   • This dual-field approach allows SegNeRF to predict not just the geometry and appearance but also the semantic information of 3D objects.
2. Experimental Validation:
   • Extensive experiments conducted on the PartNet dataset demonstrate the efficacy of SegNeRF.
   • The model achieves an average mIoU (Mean Intersection over Union) of 30.30% for 2D novel view segmentation.
   • For 3D part segmentation, SegNeRF achieves an average mIoU of 37.46%, highlighting its competitive performance compared to existing point-based methods.
3. Minimal Data Requirement:
   • A notable advantage of SegNeRF is its ability to operate with only a few posed images, a significant reduction in data requirement compared to traditional 3D modeling approaches.
   • This feature is particularly valuable for scenarios where collecting extensive image data is impractical.
4. Generalization to Unseen Objects:
   • SegNeRF can generalize to unseen objects, effectively predicting their geometry, appearance, and semantic segmentation.
5. Single Image Capability:
   • Remarkably, SegNeRF can generate an explicit 3D model with corresponding part segmentation from a single image of an object captured in the wild.

Implications:

By leveraging SegNeRF, the paper opens up new possibilities in both academic and practical applications. The ability to combine novel view synthesis, 3D reconstruction, and part segmentation within a single framework that works efficiently with minimal data marks a significant step forward. This dual capability could benefit various fields such as robotics, medical imaging, and virtual reality, where understanding both the structure and the semantic components of 3D objects is crucial.

Conclusion:

The research presents SegNeRF as a promising approach for integrating 3D part segmentation with neural radiance fields, capable of delivering competitive segmentation results while being data-efficient. This innovation bridges the gap between generative and discriminative tasks, offering a robust tool for advanced 3D object analysis.