3D-LLM: Injecting the 3D World into Large Language Models (2307.12981v1)

Abstract: LLMs and Vision-LLMs (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into LLMs and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs. Project Page: : https://vis-www.cs.umass.edu/3dLLM/.

An Analysis of 3D-LLM: Integrating the 3D World into LLMs

The integration of three-dimensional (3D) knowledge into LLMs is an emerging field of research, and the paper "3D-LLM: Injecting the 3D World into LLMs" proposes a significant stride in this direction. The authors introduce a family of models termed 3D-LLMs, designed to ingest 3D representations, such as point clouds, enabling them to perform tasks traditionally limited to vision-based systems.

With recent advancements in multi-modal vision-LLMs (VLMs), the focus has predominantly been on two-dimensional (2D) images. These models, despite their success in numerous applications such as captioning and visual question answering, do not adequately encompass the richer set of concepts found in the 3D world, like spatial relationships and object affordances. This paper seeks to bridge that gap by introducing methodologies and architectures that align 3D scene data with LLMs.

Key Innovations and Methodology

The paper outlines a comprehensive framework for embedding 3D data into LLMs. The foundational innovation is the 3D localization mechanism that incorporates spatial information directly into the language plane, enhanced with position embeddings and augmented vocabularies encoded with location tokens. This mechanism allows 3D-LLMs to interpret positions and orientations within a scene, complementing traditional semantic tasks.

The authors collect a large-scale dataset consisting of over 300,000 3D-language pairs by leveraging a series of custom prompting techniques that combine the language prowess of models like ChatGPT with 3D scene data—overcoming the scarcity challenges of 3D datasets. These data generation methods result in diverse types of annotations, covering tasks from 3D-assisted dialogues to navigation-related tasks.

Key to the model’s success is the 3D feature extractor, a system that derives 3D features from multi-view 2D images. Three methods are proposed for constructing these features: direct reconstruction from RGB-D data, feature fusion into 3D maps, and neural field construction utilizing voxel-based representations. These techniques enable seamless integration and alignment with pretrained 2D VLMs.

Strong Numerical Results

The model exhibits substantial improvements over existing state-of-the-art baselines, notably surpassing them on benchmarks such as ScanQA—a task for 3D question answering—by significant margins in performance metrics like BLEU-1, with a notable 9% improvement. The comprehensive experiments highlight the model's robustness across diverse 3D tasks beyond the scope of current LLMs and VLMs.

Interestingly, the performance gains are realized without an explicit object detection stage, which is typically a prerequisite for 3D tasks in current architectures. This indicates that the proposed 3D-LLMs can effectively capture and reason with embedded 3D spatial and semantic information.

Implications and Future Prospects

The integration of 3D data into LLMs holds substantial promise for fields requiring rich environmental understanding, such as robotics and augmented reality. The successful alignment and integration methodology proposed in this paper could inspire further research into models that can seamlessly bridge the gap between virtual and physical worlds.

Future research may explore enlarging the 3D-language datasets further, incorporating more sophisticated renderings and fine-tuning localization to achieve even higher precision in spatial reasoning tasks. Additionally, the potential for leveraging these 3D-LLMs in truly multi-modal scenarios that combine real-time video feed with direct 3D representation offers exciting avenues for exploration.

Overall, while the paper avoids sensational claims, it contributes foundational insight into embedding complex 3D world understanding within LLM frameworks, a step forward in the quest for truly intelligent AI systems capable of reasoning about their environment comparable to their fictional counterparts.