LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image
- URL: http://arxiv.org/abs/2408.07422v1
- Date: Wed, 14 Aug 2024 10:00:16 GMT
- Title: LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image
- Authors: Fan Yang, Sicheng Zhao, Yanhao Zhang, Haoxiang Chen, Hui Chen, Wenbo Tang, Haonan Lu, Pengfei Xu, Zhenyu Yang, Jungong Han, Guiguang Ding,
- Abstract summary: Current 3D perception methods, particularly small models, struggle with processing logical reasoning, question-answering, and handling open scenario categories.
We propose solutions: Spatial-Enhanced Local Feature Mining for better spatial feature extraction, 3D Query Token-Derived Info Decoding for precise geometric regression, and Geometry Projection-Based 3D Reasoning for handling camera focal length variations.
- Score: 72.14973729674995
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in autonomous driving, augmented reality, robotics, and embodied intelligence have necessitated 3D perception algorithms. However, current 3D perception methods, particularly small models, struggle with processing logical reasoning, question-answering, and handling open scenario categories. On the other hand, generative multimodal large language models (MLLMs) excel in general capacity but underperform in 3D tasks, due to weak spatial and local object perception, poor text-based geometric numerical output, and inability to handle camera focal variations. To address these challenges, we propose the following solutions: Spatial-Enhanced Local Feature Mining for better spatial feature extraction, 3D Query Token-Derived Info Decoding for precise geometric regression, and Geometry Projection-Based 3D Reasoning for handling camera focal length variations. We employ parameter-efficient fine-tuning for a pre-trained MLLM and develop LLMI3D, a powerful 3D perception MLLM. Additionally, we have constructed the IG3D dataset, which provides fine-grained descriptions and question-answer annotations. Extensive experiments demonstrate that our LLMI3D achieves state-of-the-art performance, significantly outperforming existing methods.
Related papers
- BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence [11.91274849875519]
We introduce a novel image-centric 3D perception model, BIP3D, to overcome the limitations of point-centric methods.
We leverage pre-trained 2D vision foundation models to enhance semantic understanding, and introduce a spatial enhancer module to improve spatial understanding.
In our experiments, BIP3D outperforms current state-of-the-art results on the EmbodiedScan benchmark, achieving improvements of 5.69% in the 3D detection task and 15.25% in the 3D visual grounding task.
arXiv Detail & Related papers (2024-11-22T11:35:42Z) - SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models [45.28780381341979]
We introduce a scalable situated 3D dataset, named Spartun3D, that incorporates various situated spatial reasoning tasks.
We also propose Spartun3D-LLM, built on an existing 3D-based LLM but integrated with a novel situated spatial alignment module.
arXiv Detail & Related papers (2024-10-04T19:22:20Z) - Language-Image Models with 3D Understanding [59.499585515469974]
We develop a large-scale pre-training dataset for 2D and 3D called LV3D.
Next, we introduce a new MLLM named Cube-LLM and pre-train it on LV3D.
We show that pure data scaling makes a strong 3D perception capability without 3D specific architectural design or training objective.
arXiv Detail & Related papers (2024-05-06T17:57:27Z) - OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning [68.45848423501927]
We propose a holistic framework for strong alignment between agent models and 3D driving tasks.
Our framework starts with a novel 3D MLLM architecture that uses sparse queries to lift and compress visual representations into 3D.
We propose OmniDrive-nuScenes, a new visual question-answering dataset challenging the true 3D situational awareness of a model.
arXiv Detail & Related papers (2024-05-02T17:59:24Z) - 3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp
Features and Parametric Control? [8.893200442359518]
We introduce a framework that employs Large Language Models to generate text-driven 3D shapes.
We present 3D-PreMise, a dataset specifically tailored for 3D parametric modeling of industrial shapes.
arXiv Detail & Related papers (2024-01-12T08:07:52Z) - Regulating Intermediate 3D Features for Vision-Centric Autonomous
Driving [26.03800936700545]
We propose to regulate intermediate dense 3D features with the help of volume rendering.
Experimental results on the Occ3D and nuScenes datasets demonstrate that Vampire facilitates fine-grained and appropriate extraction of dense 3D features.
arXiv Detail & Related papers (2023-12-19T04:09:05Z) - AutoDecoding Latent 3D Diffusion Models [95.7279510847827]
We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core.
The 3D autodecoder framework embeds properties learned from the target dataset in the latent space.
We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations.
arXiv Detail & Related papers (2023-07-07T17:59:14Z) - PLUME: Efficient 3D Object Detection from Stereo Images [95.31278688164646]
Existing methods tackle the problem in two steps: first depth estimation is performed, a pseudo LiDAR point cloud representation is computed from the depth estimates, and then object detection is performed in 3D space.
We propose a model that unifies these two tasks in the same metric space.
Our approach achieves state-of-the-art performance on the challenging KITTI benchmark, with significantly reduced inference time compared with existing methods.
arXiv Detail & Related papers (2021-01-17T05:11:38Z) - Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled
Representation [57.11299763566534]
We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.
We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points.
Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
arXiv Detail & Related papers (2020-04-05T12:52:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.