Empowering Any-Modality Large Models for Efficient 3D Understanding

核心概念

The core message of this paper is to propose a unified Any2Point framework that efficiently transfers any-modality large models (language, 2D vision, audio) to 3D understanding tasks through parameter-efficient fine-tuning.

摘要

The paper introduces the Any2Point framework, which aims to empower any-modality large models (language, 2D vision, audio) for efficient 3D understanding. The key components are:

3D-to-any Virtual Projection: This mechanism assigns 3D tokens with positional encodings paired with the pre-trained model, avoiding the loss of 3D geometric information caused by true projection.

Any-to-3D Guided Adapter: Inserted into each transformer block, this adapter leverages 1D/2D-guided local aggregation and adaptive any-to-3D ensemble to capture fine-grained 3D semantics.

The experiments show that the Any2Point framework achieves superior performance compared to previous 3D pre-trained models, while utilizing only 1.0% of the trainable parameters. It demonstrates the effectiveness of transferring any-modality large models to 3D tasks in an efficient manner.

統計資料

"Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios."
"Due to the scarcity of 3D data, many efforts have been made to adapt pre-trained transformers from vision to 3D domains."
"Any2Point fine-tunes only 0.8M parameters and attains 91.9% on ScanObjectNN, outperforming the previous state-of-the-art by +1.3%."
"Any2Point also achieves comparable results and efficiency by utilizing other pre-trained models of different modalities, including 2D vision, language, and audio."

引述

"To enable a general any-to-3D transferring framework, we propose Any2Point, which empowers any-modality pre-trained large models (e.g., 2D vision, language, and audio) for efficient 3D understanding."
"We introduce two techniques, i.e., 3D-to-any virtual projection and any-to-3D guided adapter, to effectively overcome the issues within current methods, such as 3D geometry loss and excessive resource cost."
"Any2Point achieves superior performance compared to previous SOTA 3D pre-trained models across various tasks. Notably, these competitive results remain consistent by leveraging pre-trained models from different modalities, e.g., 2D vision, language, and audio."

從以下內容提煉的關鍵洞見

Any2Point

by Yiwen Tang,J... 於 arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07989.pdf

深入探究

How can the Any2Point framework be extended to handle more complex 3D tasks beyond classification, such as 3D object detection and segmentation

To extend the Any2Point framework for more complex 3D tasks like object detection and segmentation, several modifications and additions can be made:

Object Detection:

Introduce region proposal networks (RPNs) to generate 3D bounding boxes for objects in the point cloud.
Implement 3D non-maximum suppression techniques to refine and consolidate object detections.
Incorporate anchor-based or anchor-free detection methods tailored for 3D data to improve localization accuracy.

Segmentation:

Utilize 3D semantic segmentation networks like PointNet++, PointCNN, or KPConv for pixel-wise classification of points in the cloud.
Implement instance segmentation techniques to differentiate between individual objects within the point cloud.
Explore hierarchical segmentation approaches to capture fine-grained details in complex 3D scenes.

Multi-Task Learning:

Introduce multi-task learning to simultaneously handle classification, detection, and segmentation tasks within the framework.
Implement task-specific adapters or branches to adapt the pre-trained models for each specific task.

Data Augmentation:

Incorporate 3D data augmentation techniques like random rotations, translations, and scaling to enhance model robustness and generalization.

By incorporating these enhancements, the Any2Point framework can be extended to tackle more intricate 3D tasks beyond simple classification, enabling it to handle object detection and segmentation with improved accuracy and efficiency.

What are the potential limitations or drawbacks of the virtual projection approach compared to true 3D projection, and how can they be addressed

The virtual projection approach in the Any2Point framework offers several advantages, such as preserving 3D geometric information and leveraging positional encodings from pre-trained models. However, there are potential limitations compared to true 3D projection:

Loss of Depth Information:

Virtual projection may not capture the depth cues present in true 3D projection, leading to potential inaccuracies in spatial relationships.

Limited Viewpoints:

Virtual projection relies on a fixed number of views or lines, which may not capture the full complexity of 3D objects from all angles.

Complexity of 3D Structures:

Virtual projection may struggle with intricate 3D structures that require a more comprehensive understanding of spatial relationships.

To address these limitations, techniques like multi-view fusion, adaptive viewpoint selection, and hierarchical feature aggregation can be incorporated. Additionally, exploring advanced projection methods and incorporating feedback mechanisms for iterative refinement can enhance the virtual projection approach's effectiveness in handling complex 3D tasks.

Given the superior performance of language models on 3D tasks, what insights can be gained about the inherent connection between language and 3D spatial understanding

The superior performance of language models on 3D tasks in the Any2Point framework highlights the inherent connection between language and 3D spatial understanding. Insights gained include:

Semantic Understanding:

Language models excel at capturing semantic relationships and contextual information, which are crucial for interpreting 3D spatial data accurately.

Positional Encoding:

The positional encodings in language models provide a structured representation of spatial information, aiding in the understanding of 3D geometries.

Cross-Modal Transfer:

The success of language models in 3D tasks showcases the potential for cross-modal transfer learning, where knowledge from one modality (language) can be effectively applied to another (3D spatial understanding).

Fine-Grained Analysis:

Language models can capture fine-grained details and nuances in 3D data, enabling more precise analysis and interpretation of complex spatial relationships.

By leveraging the strengths of language models in spatial understanding, the Any2Point framework demonstrates the power of multi-modal learning and the interconnected nature of language and 3D spatial cognition.