Foundational Models for 3D Point Clouds: A Survey and Outlook (2501.18594v1)

Abstract: The 3D point cloud representation plays a crucial role in preserving the geometric fidelity of the physical world, enabling more accurate complex 3D environments. While humans naturally comprehend the intricate relationships between objects and variations through a multisensory system, AI systems have yet to fully replicate this capacity. To bridge this gap, it becomes essential to incorporate multiple modalities. Models that can seamlessly integrate and reason across these modalities are known as foundation models (FMs). The development of FMs for 2D modalities, such as images and text, has seen significant progress, driven by the abundant availability of large-scale datasets. However, the 3D domain has lagged due to the scarcity of labelled data and high computational overheads. In response, recent research has begun to explore the potential of applying FMs to 3D tasks, overcoming these challenges by leveraging existing 2D knowledge. Additionally, language, with its capacity for abstract reasoning and description of the environment, offers a promising avenue for enhancing 3D understanding through large pre-trained LLMs. Despite the rapid development and adoption of FMs for 3D vision tasks in recent years, there remains a gap in comprehensive and in-depth literature reviews. This article aims to address this gap by presenting a comprehensive overview of the state-of-the-art methods that utilize FMs for 3D visual understanding. We start by reviewing various strategies employed in the building of various 3D FMs. Then we categorize and summarize use of different FMs for tasks such as perception tasks. Finally, the article offers insights into future directions for research and development in this field. To help reader, we have curated list of relevant papers on the topic: https://github.com/vgthengane/Awesome-FMs-in-3D.

• The paper presents a comprehensive survey of 3D foundational models using strategies like direct adaptation, dual encoders, and triplet alignment.
• It explains how 2D pre-trained models, such as ViTs and CLIP, are adapted to enhance 3D point cloud understanding in tasks like segmentation and detection.
• It highlights future research directions including the need for larger 3D datasets and efficient adaptation techniques for robust real-world applications.

Foundational Models for 3D Point Clouds: A Survey and Outlook

The exploration of foundational models (FMs) for 3D point cloud data offers a promising avenue toward enhancing artificial intelligence systems' capacities to comprehend and interact with the three-dimensional world. While significant strides have been made in applying FMs to 2D modalities like images and text, there remains a discernible gap in the literature concerning their adaptation and application within the 3D domain. This work explores this gap by surveying methodologies that leverage FMs for 3D visual understanding, with particular emphasis on point clouds, a pivotal representation form for 3D data.

3D point clouds, composed of unordered sets of 3D coordinates often enriched with additional attributes (such as RGB values), have emerged as essential for tasks across computer vision, robotics, and augmented reality. Despite their potential, the domain faces challenges, primarily due to limited availability of large-scale 3D datasets and the computational cost associated with data acquisition and processing. This scarcity has necessitated the innovative use of 2D modalities, prompting the emergence of methods that aim to transfer knowledge from 2D to 3D domains.

Strategies for Building 3D FMs

The surveyed paper categorizes existing methods for building 3D FMs into three main strategies: direct adaptation, dual encoders, and triplet alignment.

1. Direct Adaptation: Techniques in this category directly incorporate 2D pre-trained models, such as ViTs and CLIP, into 3D tasks. Methods like Image2Point and PointCLIP leverage 2D image features to enhance the interpretive power of 3D models. By expanding 2D architectures to process point cloud data, these approaches illustrate the potential of existing 2DFMs in the 3D field, often requiring minimal adjustment to handle the inherent differences in data representation.
2. Dual Encoders: This approach involves parallel processing streams, where one encoder processes 3D data and the other handles 2D data. Models like CrossPoint achieve cross-modal feature alignment through contrastive learning, thereby enabling the transfer of semantic understanding from 2D pre-trained models to 3D representations, enhancing downstream 3D tasks such as segmentation and detection.
3. Triplet Alignment: Focusing on simultaneous alignment of text, images, and 3D point cloud representations, this approach seeks to establish a unified feature space leveraging triplet data inputs. Methods such as ULIP and OpenShape illustrate the efficacy of this strategy in achieving a more integrated understanding of 3D environments, facilitating open-world classification and reasoning tasks.

Application and Implications

These foundational models have demonstrated potential not only in classical 3D understanding tasks like object classification and segmentation but also in open-vocabulary and multi-modal contexts, which require the integration of diverse data modalities. The implications of this paper suggest that robust methods for adapting FMs can significantly mitigate the challenges posed by the scarcity of 3D data by harnessing pre-trained knowledge from 2D and LLMs.

Future Directions

The survey highlights several avenues for future research. One key area is the development of more comprehensive and diverse 3D datasets, mirroring the size and complexity of current 2D datasets, to better train and evaluate FMs. Additionally, enhancing the scalability and generalization abilities of 3D FMs to effectively tackle larger and more variable real-world environments remains a challenge. Furthermore, exploring efficient adaptation techniques and continued learning paradigms could enable these models to adapt dynamically to new data or tasks without requiring extensive retraining.

By providing a structured overview of existing methodologies and their applications, this survey sets the stage for further advancements in the field of 3D world understanding using foundational models, pointing towards a future where AI systems could achieve more profound and nuanced interactions with the physical environment.