Research map / Vision-language and multimodal / Multimodal LLMs
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Introduces LiDAR-LLM, which feeds raw LiDAR BEV features through a View-Aware Transformer into a frozen LLaMA-based LLM, trained in three stages on newly generated nuScenes LiDAR-text data for captioning, grounding, and QA.
Problem
Existing multimodal LLMs handle 2D images and indoor point clouds but not sparse outdoor LiDAR scenes, and LiDAR-text paired data is scarce.
Approach
Reformulate outdoor 3D LiDAR scene understanding as language modeling by aligning BEV LiDAR features with an LLM via a View-Aware Transformer that injects six learnable view position embeddings, trained with a three-stage curriculum (cross-modal alignment via captioning, perception via grounding, high-level instruction tuning).
Key results
- On nu-Caption, LiDAR-LLM reaches 40.98 BLEU-1 and 91.32 BERT Score, versus 30.17 BLEU-1 for the best baseline LLaMA-AdapterV2.
- On nu-Grounding, 5-category accuracy is 63.1% (vs. 23.9% for InstructBLIP) and car BEV mIoU is 14.3%.
- On nuScenes-QA, the full three-stage pipeline (Ex4) reaches 48.6% accuracy vs. 41.2% training from scratch.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use LiDAR-LLM instead of the strongest listed baseline, LLaMA-AdapterV2 | 3D captioning · nu-Caption | BLEU-4: 7.45 -> 19.26 | Helped |
| use LiDAR-LLM instead of Instruct-BLIP | 3D grounding · nu-Grounding, 5 categories | ACC-5: 23.9 -> 63.1 | Helped |
| add captioning pretraining (ablation) | LiDAR-LLM · nuScenes-QA | accuracy: 41.2 -> 47.4 | Helped |
| add grounding pretraining (ablation) | LiDAR-LLM · nuScenes-QA | accuracy: 41.2 -> 46.5 | Helped |
| add both captioning and grounding pretraining instead of either one alone (ablation) | LiDAR-LLM · nuScenes-QA | accuracy: 48.6 | Helped |
| add the Query Transformer (ablation) | LiDAR-LLM · nu-Caption | BERT Score: 88.14 -> 90.60 | Helped |
| add the Query Transformer (ablation) | LiDAR-LLM · nu-Caption | BLEU-4: 11.37 -> 15.41 | Helped |
| add View Position Embedding to the Query Transformer (ablation) | LiDAR-LLM · nu-Caption | BERT Score: 90.60 -> 91.32 | Helped |
| add View Position Embedding to the Query Transformer (ablation) | LiDAR-LLM · nu-Caption | BLEU-4: 15.41 -> 19.26 | Helped |
Surprising findings
- Pretraining on captioning and grounding both improves nuScenes-QA accuracy, but the view position embedding ablation shows the VAT gains are concentrated in captioning metrics.
- Directly applying 2D MLLMs to projected LiDAR depth maps performs poorly, suggesting 2D MLLMs are not adequate for LiDAR scenes.
Limitations
- Relies on GPT-4 and 2D MLLM generated captions, which may contain image-only content such as weather or color that LiDAR cannot capture.
- Grounding mIoU is low (14.3% for cars), and the evaluation covers only nuScenes.
- Baselines are 2D MLLMs applied to projected LiDAR depth rather than dedicated 3D models.
Experimental setup
Datasets: nuScenes nuScenes-QA nu-Caption nu-Grounding
Benchmarks: nu-Caption nu-Grounding nuScenes-QA
Metrics: BLEU-1 BLEU-4 BERT Score Top-1 accuracy BEV mIoU
Baselines: MiniGPT-4 LLaVA-1.5 InstructBLIP LLaMA-AdapterV2
Builds on: BLIP-2 LLaMA VoxelNet CenterPoint nuScenes-QA
Closest papers by meaning
- Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding (CVPR 2025 · similarity 0.90)
- More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding (AAAI 2025 · similarity 0.90)
- 3D-PLOT-LLM: Part-Level Object Tokens for 3D Large Language Models (NeurIPS 2026 · similarity 0.89)
- Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors (NeurIPS 2025 · similarity 0.89)
- Language-Image Models with 3D Understanding (ICLR 2025 · similarity 0.89)
- Vid-LLM: A Compact Video-based 3D Multimodal LLM with Reconstruction–Reasoning Synergy (ICLR 2026 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.