Efficient Non-Parametric Networks for Few-shot 3D Scene Segmentation

핵심 개념

The authors propose efficient non-parametric and parametric frameworks, Seg-NN and Seg-PN, for few-shot 3D scene segmentation. Seg-NN is a training-free encoder that can extract discriminative representations without any learnable parameters, while Seg-PN further improves performance with a lightweight query-support transferring module.

초록

The authors address the data-hungry problem in 3D scene segmentation by exploring few-shot learning strategies. They propose two efficient frameworks, Seg-NN and Seg-PN, to tackle the few-shot 3D segmentation task.
Seg-NN:

Adopts a non-parametric encoder that extracts dense representations using hand-crafted filters without any learnable parameters.
Discards the pre-training and episodic training stages, which saves substantial time and resources and mitigates the domain gap between seen and unseen classes.
Achieves comparable performance to existing parametric models while being more efficient.
Seg-PN:

Inherits the non-parametric encoder from Seg-NN and introduces a lightweight Query-Support Transferring (QUEST) module.
QUEST enhances the interaction between the support set and query set, suppressing prototype biases caused by the small few-shot support set.
Outperforms previous state-of-the-art methods by a large margin on S3DIS and ScanNet datasets, while reducing the training time by over 90%.
Experiments demonstrate the effectiveness and efficiency of the proposed frameworks, achieving new state-of-the-art results with significantly simplified training pipelines.

통계

Seg-NN achieves +7.16% and +9.81% mIoU improvement over Point-NN on 2-way-1-shot and 3-way-1-shot tasks on S3DIS, respectively.
Seg-PN outperforms the second-best method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets, respectively.
Seg-PN reduces the training time by over 90% compared to existing methods.

인용구

"To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning."
"Seg-NN discards all two stages of pre-training and episodic training and performs comparably to some existing parametric methods."
"Seg-PN only learns the QUEST module and does not require pre-training just as Seg-NN, as shown in Fig. 1 (b)."

핵심 통찰 요약

No Time to Train

by Xiangyang Zh... 게시일 arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04050.pdf

더 깊은 질문

How can the proposed non-parametric encoder be further extended to other 3D tasks beyond segmentation, such as classification and part segmentation

The proposed non-parametric encoder in the context can be extended to other 3D tasks beyond segmentation by adapting its architecture and training scheme. For classification tasks, the encoder can be modified to output global representations for each point cloud by incorporating global pooling layers. These global representations can then be used for similarity-based classification, similar to the segmentation task. Additionally, for part segmentation, the encoder can be adjusted to generate prototypes for each part category within an object. By utilizing the similarity-based segmentation approach, the encoder can assign each point to its corresponding part category based on the similarity to the part prototypes. This extension allows the non-parametric encoder to handle a variety of 3D tasks efficiently and effectively.

What are the potential limitations of the QUEST module in Seg-PN, and how can it be improved to better capture the complex interactions between the support and query sets

The QUEST module in Seg-PN may have limitations in capturing complex interactions between the support and query sets. One potential limitation is the reliance on self-correlation and cross-correlation alone, which may not fully capture the intricate relationships between the support and query data. To improve the module, additional attention mechanisms or graph neural networks can be incorporated to enhance the interaction modeling. By introducing attention mechanisms, the module can focus on relevant parts of the support set when adjusting the prototypes for the query set, leading to more accurate and context-aware adjustments. Moreover, incorporating graph neural networks can enable the module to capture spatial dependencies and structural information within the support and query sets, further enhancing the adaptation and interaction capabilities of the module.

Given the efficiency of the proposed frameworks, how can they be deployed in real-world 3D applications, such as autonomous driving or robotics, to enable rapid adaptation to new environments or tasks

The proposed frameworks, Seg-NN and Seg-PN, can be deployed in real-world 3D applications such as autonomous driving or robotics to enable rapid adaptation to new environments or tasks. In autonomous driving, the frameworks can be utilized for scene understanding, object detection, and localization tasks. By leveraging the efficient and training-free nature of Seg-NN and Seg-PN, autonomous vehicles can quickly adapt to new road conditions, obstacles, and environments without the need for extensive retraining. Similarly, in robotics applications, the frameworks can be employed for object recognition, manipulation, and navigation tasks. The ability to perform few-shot learning and adapt to new scenarios rapidly makes Seg-NN and Seg-PN valuable tools for real-time decision-making and task execution in dynamic and changing environments.