Keskeiset käsitteet
This paper proposes a novel method for 3D scene understanding that leverages 2D panoptic segmentation information within a neural radiance field framework, guided by perceptual priors, to achieve accurate and consistent 3D panoptic segmentation.
Tiivistelmä
Bibliographic Information:
Li, S. (2021). In-Place Panoptic Radiance Field Segmentation with Perceptual Prior for 3D Scene Understanding. JOURNAL OF LATEX CLASS FILES, 14(8).
Research Objective:
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.
Methodology:
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.
Key Findings:
- The proposed method achieves state-of-the-art results on multiple indoor and outdoor scene datasets, demonstrating its effectiveness in handling various scene characteristics and challenging conditions.
- The use of perceptual priors significantly improves the accuracy and consistency of 3D panoptic segmentation, particularly in scenes with boundary ambiguity.
- The proposed implicit scene representation and understanding model effectively captures both geometric and semantic information, enabling accurate 3D reconstruction and panoptic understanding.
Main Conclusions:
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.
Significance:
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.
Limitations and Future Research:
- The proposed method relies on pre-trained 2D panoptic segmentation networks, which may limit its performance in scenarios with novel object categories or unseen environments.
- The computational complexity of the method could be further optimized for real-time applications.
- Future research could explore the integration of other sensory modalities, such as depth or lidar data, to further enhance the robustness and accuracy of 3D scene understanding.
Tilastot
The proposed method achieves a PSNR of 36.6 on the Replica dataset, outperforming all baseline methods.
On the HyperSim dataset, the proposed method achieves a PQscene score of 67.2, demonstrating its effectiveness in large-scale indoor environments.
The proposed method achieves an mIOU of 63.4 on the KITTI-360 dataset, highlighting its ability to handle challenging outdoor scenes with boundary ambiguity.
Lainaukset
"To overcome these challenges, a perceptual prior guided 3D scene representation and panoptic understanding method is proposed in this paper."
"The proposed method formulates the panoptic understanding of neural radiance fields as a linear assignment problem from 2D pseudo labels to 3D space."
"By incorporating high-level features from pre-trained 2D panoptic segmentation models as prior guidance, the learning processes of appearance, geometry, semantics, and instance information within the neural radiance field are synchronized."