Panoptic Vision-Language Feature Fields (2309.05448v2)

Abstract: Recently, methods have been proposed for 3D open-vocabulary semantic segmentation. Such methods are able to segment scenes into arbitrary classes based on text descriptions provided during runtime. In this paper, we propose to the best of our knowledge the first algorithm for open-vocabulary panoptic segmentation in 3D scenes. Our algorithm, Panoptic Vision-Language Feature Fields (PVLFF), learns a semantic feature field of the scene by distilling vision-language features from a pretrained 2D model, and jointly fits an instance feature field through contrastive learning using 2D instance segments on input frames. Despite not being trained on the target classes, our method achieves panoptic segmentation performance similar to the state-of-the-art closed-set 3D systems on the HyperSim, ScanNet and Replica dataset and additionally outperforms current 3D open-vocabulary systems in terms of semantic segmentation. We ablate the components of our method to demonstrate the effectiveness of our model architecture. Our code will be available at https://github.com/ethz-asl/pvlff.

Panoptic Vision-Language Feature Fields: Toward 3D Open-Vocabulary Panoptic Segmentation

The paper "Panoptic Vision-Language Feature Fields" introduces an innovative approach to 3D open-vocabulary panoptic segmentation. The proposed method, Panoptic Vision-Language Feature Fields (PVLFF), extends the capabilities of existing neural field representations to simultaneously handle semantic and instance segmentation in open-vocabulary contexts. This research is particularly significant as it addresses the challenge of segmenting 3D scenes into arbitrary classes based on text descriptions not seen during training, thereby advancing the field of robotics and augmented reality.

Methodology Overview

PVLFF builds upon the neural radiance field framework, such as NeRF, and leverages contrastive learning to distill rich semantic information from pre-trained 2D vision-LLMs. It presents a novel two-branch architecture: one for a semantic feature field and another for an instance feature field. The semantic feature field is constructed by distilling vision-language embeddings from an off-the-shelf network, while the instance feature field employs contrastive learning from 2D instance proposals derived from a dense segmentation model. This design allows PVLFF to perform robust panoptic segmentation by encoding both semantic and instance-level features in 3D space.

Key Results

PVLFF demonstrates performance comparable to state-of-the-art closed-set systems on datasets such as HyperSim, ScanNet, and Replica. This is achieved despite the model's open-vocabulary nature, which means it is not trained on specific target classes. In semantic segmentation, PVLFF outperforms existing zero-shot methods, registering a +4.6% improvement in mean Intersection over Union (mIoU), thereby validating the efficacy of its panoptic segmentation capabilities. These results underline the system's potential for flexible query-based scene understanding without the need for retraining on specific class annotations.

Implications and Future Work

The implications of this research are profound for the development of AI systems that require dynamic understanding and manipulation of 3D environments. By enabling open-vocabulary panoptic segmentation, PVLFF significantly enhances the adaptability and intelligence of robotic systems in complex, real-world scenarios. The method's ability to segment hierarchical instances is particularly promising for applications in mobile manipulation and autonomous systems, where fine-grained scene understanding is critical.

For future research, extending PVLFF to improve query-dependent instance segmentation, optimizing the feature representation for broader vocabulary sets, and experimenting with alternative vision-LLMs could further enhance its performance. Additionally, exploring the integration of PVLFF with downstream robotic tasks, such as navigation and object manipulation, could provide valuable insights into its real-world applicability.

Overall, the authors present a robust framework that demonstrates significant advancements in semantic scene understanding and lays a strong foundation for future exploration in the field of autonomous systems and artificial intelligence.