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LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Senqiao Yang, Jiaming Liu, Ray Zhang, Mingjie Pan, Zoey Guo, Xiaoqi Li, Zehui Chen, Peng Gao, Yandong Guo, Shanghang Zhang

arXiv:2312.14074v1cs.CV

TL;DR

Outdoor 3D scene understanding with LLMs remains an open challenge, especially for sparse LiDAR data and its complex object relationships. LiDAR-LLM uses staged training and a View-Aware Transformer to align LiDAR with language representations. It reports results across captioning and grounding tasks, including 40.9 BLEU-1 on captioning.

  • Problem

    LLMs and multimodal models have shown promise for language and 2D images, but understanding challenging 3D real-world scenes—particularly sparse outdoor LiDAR—remains an open question.

    Source: §1 · p. 2

  • Method

    LiDAR-LLM reformulates outdoor 3D scene cognition as language modeling, using generated LiDAR-text datasets, three-stage training, and a View-Aware Transformer to align LiDAR features with LLM representations.

    Source: §1 · p. 2, §3.2 · p. 4

  • Results

    LiDAR-LLM reports 40.9 BLEU-1 on 3D captioning, 63.1% classification accuracy, and 14.3% BEV mIoU on 3D grounding.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The authors report that LiDAR-LLM can comprehend varied 3D-scene instructions and engage in complex spatial reasoning.

    Source: §1 · p. 2, §5 · p. 9

Abstract

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Recently, Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have shown promise in instruction following and 2D image understanding. While these models are powerful, they have not yet been developed to comprehend the more challenging 3D physical scenes, especially when it comes to the sparse outdoor LiDAR data. In this paper, we introduce LiDAR-LLM, which takes raw LiDAR data as input and harnesses the remarkable reasoning capabilities of LLMs to gain a comprehensive understanding of outdoor 3D scenes. The central insight of our LiDAR-LLM is the reformulation of 3D outdoor scene cognition as a language modeling problem, encompassing tasks such as 3D captioning, 3D grounding, 3D question answering, etc. Specifically, due to the scarcity of 3D LiDAR-text pairing data, we introduce a three-stage training strategy and generate relevant datasets, progressively aligning the 3D modality with the language embedding space of LLM. Furthermore, we design a View-Aware Transformer (VAT) to connect the 3D encoder with the LLM, which effectively bridges the modality gap and enhances the LLM's spatial orientation comprehension of visual features. Our experiments show that LiDAR-LLM possesses favorable capabilities to comprehend various instructions regarding 3D scenes and engage in complex spatial reasoning. LiDAR-LLM attains a 40.9 BLEU-1 on the 3D captioning task and achieves a 63.1\% classification accuracy and a 14.3\% BEV mIoU on the 3D grounding task. Web page: https://sites.google.com/view/lidar-llm

1. Introduction

Understanding outdoor 3D scenes with LLMs remains challenging, particularly for sparse LiDAR data. LiDAR-LLM treats scene cognition as language modeling and combines staged training data with a View-Aware Transformer to align LiDAR and language.

1. Introduction

  • Outdoor LiDAR understanding remains difficult because LiDAR-text pairs are scarce and the data contains diverse objects with intricate geometric relationships.Existing 3D methods may generalize poorly to unseen scenarios, while prior LLM-based 3D work described here focused on indoor point clouds.Source: §1 · p. 2
  • LiDAR-LLM reformulates outdoor 3D scene cognition as language modeling, using an encoder, an intermediate alignment transformer, and an LLM.The intended tasks include 3D captioning, grounding, and question answering.Source: §1 · p. 2
  • The three-stage training strategy progressively aligns LiDAR representations with text through cross-modal alignment, perception, and high-level instruction.The generated datasets include 420K captioning pairs and 280K grounding pairs.Source: §1 · p. 2
  • The View-Aware Transformer connects the LiDAR encoder and LLM, adding six view-position embeddings to improve spatial-orientation comprehension.The reported results include 40.9 BLEU-1 for 3D captioning, plus 63.1% classification accuracy and 14.3% BEV mIoU for grounding.Source: §1 · p. 2

2. Related Work

Prior multimodal language models extended LLMs to visual inputs, while 3D-language work addressed point-cloud tasks, often in indoor or task-specific settings. LiDAR-LLM targets a unified approach for outdoor LiDAR scenes.

2.1. Multi-modal Large Language Models

  • 2D multimodal models condition LLMs on images, while 3D multimodal work extends language-based reasoning to point clouds, including single objects and indoor scenes.The cited examples include BLIP, VisionLLM, PointCLIP V2, and ViewRefer.Source: §2.1 · pp. 2–3
  • The paper positions LiDAR-LLM as an approach to outdoor 3D understanding with captioning, grounding, and question answering, where LiDAR-text scarcity and complex relationships challenge alignment and reasoning.The authors describe it as the first use of LLM reasoning for outdoor 3D scenes.Source: §2.1 · p. 3

2.2. 3D-Language Tasks

  • 3D captioning describes an object in a scene, visual grounding locates the object referred to by text, and 3D question answering answers language questions from scene content.The cited task references are 3D captioning [11] [13], visual grounding [10] [48], and visual question answering [5].Source: §2.2 · p. 3
  • Earlier methods addressed these tasks individually and were tailored to indoor point clouds, which may not transfer directly to sparser outdoor LiDAR with more diverse geometric relationships.LiDAR-LLM is proposed to perform outdoor 3D tasks uniformly.Source: §2.2 · p. 3

3. Method

LiDAR-LLM maps sparse LiDAR features into an LLM-compatible representation through a View-Aware Transformer and staged training. The stages progress from scene captioning to object perception and high-level instruction, followed by joint fine-tuning for varied tasks.

3.1. Overview

  • LiDAR-LLM aligns sparse, geometrically complex LiDAR representations with the LLM feature space, using VAT view-position embeddings to support spatial orientation understanding.The training data and stages address scarce paired supervision and support scene- and instance-level tasks.Source: §3.1 · pp. 3–4

3.2. Model Architecture

  • VoxelNet extracts 3D voxel features, which are flattened along z into BEV features that VAT projects into the pre-trained LLaMA word-embedding space.Training fine-tunes injected LLaMA adapters and VAT while freezing the major parameters.Source: §3.2 · p. 4
  • VAT uses 576 learnable queries with cross-attention to BEV features, then passes encoded visual vectors through an MLP to the frozen LLM.Six view embeddings are injected into the BEV features and queries for view-specific samples, while panoramic samples use all six.Source: §3.2 · p. 4

3.3. Three-stage training strategy

  • Caption training uses filtered multi-view-image descriptions paired with LiDAR, first teaching single-view captions and then global panoramic descriptions.GPT-4 filters captions that describe image properties such as weather or color that are not applicable to LiDAR; the staged progression is intended to reduce complexity before global captioning.Source: §3.3 · pp. 4–5
  • The perception stage aligns object representations with text using visual grounding and grounded captioning across the entire 3D scene.Grounding generates location tokens from LiDAR and instructions; grounded captioning generates descriptive text using LiDAR and text with location information.Source: §3.3 · p. 5

3.4. Training and Task Inference.

  • High-level instruction fine-tuning uses datasets for 3D reasoning and planning exploration, while joint training combines 700K generated and 460K public LiDAR-text pairs for flexible question inference.The paper reports preliminary planning capabilities and allows questions to be inferred individually or consecutively.Source: §3.3 · p. 5, §3.4 · p. 5

4. Experiment

The experiments assess LiDAR-LLM on 3D captioning, grounding, and high-level instruction following, alongside ablations and qualitative analysis.

4. Experiment

  • Experiments cover nuScenes, nuScenes-QA, and generated datasets, evaluating captioning, grounding, instruction following, ablations, and qualitative behavior.The paper also points to appendices for additional quantitative and qualitative analyses.Source: §4 · p. 5

4.1. Baselines & Evaluation Metrics

  • The evaluation uses MiniGPT-4 and LLaMA-Adapter V2 as 2D MLLM counterparts, with language-generation, grounding, and question-answering metrics.Metrics include BLEU and BERT Score for captioning, Top-1 accuracy and BEV mIoU for grounding, and Top-1 accuracy for NuSceneQA.Source: §4.1 · p. 5

4.2. Implementation Details

  • Implementation uses a pre-trained CenterPoint-Voxel backbone, 576 VAT queries, and LLaMA-7B, with VAT and LLaMA adapters fine-tuned for six epochs.Experiments were conducted on NVIDIA Tesla A100 GPUs.Source: §4.2 · p. 6

4.3. 3D Captioning

  • 19.26% BLEU-4 and 91.32% Bert score were achieved on nu-Caption, with LiDAR-LLM outperforming all baselines across reported metrics.The text compares these results with MiniGPT4 and reports improvements over LLaMA-AdapterV2.Source: §4.3 · p. 6, Table 1 · p. 6

4.4. 3D Grounding

  • 63.1% accuracy in 5-category grounded captioning and 14.3% BEV mIoU for Car were reported on nu-Grounding.The 5-category accuracy exceeds the reported LLaMA-Adapter and MiniGPT4 results.Source: §4.4 · pp. 6–7, Table 2 · p. 6

4.5. High-level Instruction Task

  • 48.6% total accuracy follows pre-training on both captioning and grounding, with gains on zero-hop and one-hop nuScenes-QA questions.The study reports that pre-training on either task also improves accuracy over training from scratch.Source: §4.5 · p. 7, Table 3 · p. 7

4.6. Ablation Study

  • 3.85% BLEU-4 improvement followed adding View Position Embedding, alongside a 0.7% BERT score improvement over the query-transformer comparison.The ablation compares VAT components on 3D captioning.Source: §4.6 · p. 7, Table 4 · p. 7

4.7. Qualitative Analysis

  • Qualitative examples show captioning of object states and relationships, referred-object localization, planning instructions, and NuScenes-QA counting and spatial reasoning.One planning example identifies an obstructing bus and recommends braking and stopping to avoid collision.Source: §4.7 · pp. 7–8, Figure 3 · p. 8

5. Conclusion

LiDAR-LLM reframes outdoor 3D scene understanding from LiDAR as language modeling, using generated paired data, three-stage training, and a View-Aware Transformer. The paper reports promising performance across several scene-understanding tasks and identifies continual transfer learning and lightweight operations as future directions.

5. Conclusion

  • LiDAR-LLM reformulates outdoor 3D scene understanding as a language-modeling problem and reports promising performance across captioning, grounding, question answering, and autonomous-driving planning.Source: §5 · p. 9
  • The training data comprise 420K 3D captioning examples and 280K 3D grounding examples, used in a three-stage strategy to align LiDAR with the LLM language embedding space.Source: §5 · p. 9
  • The View-Aware Transformer connects the 3D encoder to the LLM and is designed to bridge the modality gap and enhance spatial-orientation comprehension of LiDAR features.Source: §5 · p. 9
  • Future work will explore continual transfer learning and lightweight operations for making MLLMs feasible to deploy on edge devices.Source: §5 · p. 9

A. Additional Experiments

The appendix adds baseline comparisons and broadens the visual-grounding experiment to more categories.

A. Additional Experiments

A.1. 3D Captioning

On nu-Caption, LiDAR-LLM outperforms the compared 2D MLLM baseline, with the authors attributing the difference to the importance of 3D information for describing sparse LiDAR scenes.

A.1. 3D Captioning

  • 19.26% BLEU-4 and 91.32% Bert score are reported for LiDAR-LLM, with 11.81% BLEU-4 and 4.87% Bert score improvements over LLaMA-AdapteV2 on nu-Caption.Source: §A.1 · p. 12
  • The authors report that directly applying 2D MLLMs to LiDAR produces unsatisfactory captions that omit vital information.Source: §A.1 · p. 12
  • The findings support LiDAR-LLM’s ability to capture geometric relationships and perform common reasoning with sparse LiDAR data.Source: §A.1 · p. 12

A.2. 3D Grounding

The additional grounding experiments report classification and localization results across five- and 19-category settings, alongside a box-regression design for producing more accurate locations.

A.2. 3D Grounding

  • 63.1% accuracy on five-category grounded captioning exceeds Instruct-BLIP’s 23.9% accuracy.Source: §A.2 · p. 12
  • With 19 categories, LiDAR-LLM retains an advantage of more than 25% over 2D MLLMs, and the authors associate its grounding results with localization understanding and object classification under LiDAR.Source: §A.2 · p. 12
  • Expanding visual grounding to five categories does not decrease BEV mIoU; the challenging pedestrian category reaches 9.05% mIoU.Source: §A.2 · p. 12
  • For bounding boxes, the method projects the LLM’s last hidden state through an MLP and optimizes with L2 regression loss rather than tokenizer-based classification loss.Source: §A.2 · p. 12

A.3. High-level Instruction Task

On nuScenes-QA, LiDAR-LLM outperformed previous 2D MLLM methods on the reported status-task comparison, supporting its ability to handle high-level reasoning tasks.

A.3. High-level Instruction Task

  • 45.9% status-task accuracy, compared with 9.0% for the previous SOTA method, was reported for LiDAR-LLM on nuScenes-QA.The comparison is reported in Table 8.Source: §A.3 · p. 12
  • The nuScenes-QA evaluation compared LiDAR-LLM with previous 2D SOTA MLLM models.The passage highlights the status task as a comparison where LiDAR-LLM had an advantage.Source: §A.3 · p. 12
  • The authors report that LiDAR-LLM can effectively handle high-level reasoning tasks.Source: §A.3 · p. 12

B. Additional Qualitative Analysis

The qualitative examples show coherent scene interpretation and risk-aware action generation, alongside errors linked to LiDAR’s limited semantic and color information.

B. Additional Qualitative Analysis

  • The model’s captions identify a tree-lined city street and sparse traffic, which informs a suggested safe action to go ahead.The authors describe the action as avoiding unsafety risks.Source: B. Additional Qualitative Analysis · p. 12
  • Limited LiDAR semantics can produce errors, including describing a clear scene as cloudy and predicting a nonexistent red traffic light.The authors attribute these examples to absent color information and note other grounding and high-level action errors.Source: B. Additional Qualitative Analysis · pp. 12–13
  • LiDAR-LLM outperforms all baseline models on the nu-Caption dataset across the reported evaluation metrics.The supplied table caption does not provide metric values.Source: Table 5 · p. 13

C. LiDAR-Language Data

The LiDAR-language datasets cover progressively more complex scene captions, caption-aligned questions, and instance-level grounding tasks.

C. LiDAR-Language Data

  • nu-Caption progresses from broad scene or traffic descriptions to detailed object relationships and potential road risks.Source: C. LiDAR-Language Data · p. 13
  • LLaMA-Adapter and GPT-4 are used to structure, refine, and extend questions and QA pairs aligned with captions.The passage reports 349K training pairs and 72K validation pairs.Source: C. LiDAR-Language Data · p. 13
  • nu-Grounding includes grounded captioning of localized areas and visual grounding that returns bounding boxes for queried objects.Its boxes use seven values: xmin, ymin, zmin, xmax, ymax, zmax, and angle; the data come from nuScenes ground truth.Source: C. LiDAR-Language Data · p. 14
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