PointLLM: Empowering Large Language Models to Understand Point Clouds (2308.16911v3)

Abstract: The unprecedented advancements in LLMs have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding. This paper introduces PointLLM, a preliminary effort to fill this gap, enabling LLMs to understand point clouds and offering a new avenue beyond 2D visual data. PointLLM understands colored object point clouds with human instructions and generates contextually appropriate responses, illustrating its grasp of point clouds and common sense. Specifically, it leverages a point cloud encoder with a powerful LLM to effectively fuse geometric, appearance, and linguistic information. We collect a novel dataset comprising 660K simple and 70K complex point-text instruction pairs to enable a two-stage training strategy: aligning latent spaces and subsequently instruction-tuning the unified model. To rigorously evaluate the perceptual and generalization capabilities of PointLLM, we establish two benchmarks: Generative 3D Object Classification and 3D Object Captioning, assessed through three different methods, including human evaluation, GPT-4/ChatGPT evaluation, and traditional metrics. Experimental results reveal PointLLM's superior performance over existing 2D and 3D baselines, with a notable achievement in human-evaluated object captioning tasks where it surpasses human annotators in over 50% of the samples. Codes, datasets, and benchmarks are available at https://github.com/OpenRobotLab/PointLLM .

• The paper introduces PointLLM, a novel model that integrates colored point clouds with LLMs to address challenges in 3D object understanding.
• The methodology employs a point cloud encoder, a projector, and an LLM backbone, with a two-stage training process for feature alignment and instruction tuning.
• Evaluation demonstrates that PointLLM outperforms traditional 2D and 3D models, achieving over 50% human-level performance on 3D object captioning tasks.

PointLLM: Empowering LLMs to Understand Point Clouds

PointLLM introduces an advancement in the integration of LLMs with 3D data, specifically colored point clouds. This paper fills a significant gap in multimodal AI capabilities, addressing the challenges associated with 3D object understanding, including issues like ambiguous depth, occlusion, and viewpoint dependency prevalent in 2D visual data applications.

Introduction to PointLLM

The inspiration for PointLLM arises from the limitations of current LLMs when applied to 3D structure understanding. While LLMs have demonstrated versatility across text, image, and even multimodal domains, the leap to 3D structures involves unique challenges. The paper emphasizes the potential applications in interactive 3D modeling, robotics, and other fields requiring an intrinsic understanding of 3D objects.

PointLLM is designed to process colored point clouds, augmenting its comprehension of object types, geometries, and visual appearances without the complications tied to depth and perspective.

Figure 1: We introduce PointLLM, a multi-modal LLM capable of understanding colored point clouds of objects.

Methodology

The core architecture of PointLLM consists of a point cloud encoder, a projector, and an LLM backbone. The point encoder transforms input point cloud data into a sequence of features, which the projector maps into the latent space of the LLM. This integration forms the basis of the model's ability to process and generate responses from point cloud and text data effectively.

Figure 2: An overview of PointLLM. The point encoder extracts features from the input point cloud and the projector projects them to the latent space of the LLM backbone.

Training Strategy

The training of PointLLM follows a two-stage process:

1. Feature Alignment: This stage aligns the latent spaces between the encoder and the LLM, utilizing brief-description instructions generated by the model.
2. Instruction Tuning: In this stage, the model is fine-tuned with complex instructions to enhance its capability to respond to diverse queries accurately.

Evaluation and Results

Two benchmarks were proposed to evaluate PointLLM's performance: Generative 3D Object Classification and 3D Object Captioning. These benchmarks assess the model's understanding and generalization capabilities in generating detailed, semantically meaningful outputs.

The results from experimental evaluations demonstrate PointLLM's superiority over existing 2D and 3D models. Notably, in 3D Object Captioning, PointLLM surpasses human performance in over 50% of test samples based on human evaluations.

Figure 3: Win rate comparison. PointLLM outperforms human annotations in more than half of the testing samples and exhibits a substantial advantage over other models.

Ablation Studies

Ablation studies conducted in the paper provide insights into the effectiveness of different components and configurations within PointLLM. The analysis highlights the importance of data quantity for feature alignment and the advantage of certain architectural decisions, such as the number of projection layers and training data diversity.

Figure 4: Ablation on data for alignment.

Conclusion

The development of PointLLM marks a significant step forward in multimodal AI, enhancing the interpretative capabilities of LLMs with respect to 3D data. By facilitating a nuanced understanding of complex structures within point clouds, PointLLM opens new avenues for applications in 3D modeling, robot-human interaction, and other fields requiring spatial understanding.

Continued research may focus on extending PointLLM's capabilities to other forms of 3D data representation, further exploration into model efficiency, and enhanced training techniques to improve its generalization and performance across diverse tasks. The release of resources such as codes, datasets, and benchmarks promises to foster further innovation and applications in this area.