Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding
- URL: http://arxiv.org/abs/2412.00493v1
- Date: Sat, 30 Nov 2024 14:28:53 GMT
- Title: Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding
- Authors: Duo Zheng, Shijia Huang, Liwei Wang,
- Abstract summary: Video-3D LLM treats 3D scenes as dynamic videos and incorporates 3D position encoding into these representations.<n>Our model achieves state-of-the-art performance on several 3D scene understanding benchmarks.
- Score: 19.382210260928776
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly impacted various multimodal tasks. However, these models face challenges in tasks that require spatial understanding within 3D environments. Efforts to enhance MLLMs, such as incorporating point cloud features, have been made, yet a considerable gap remains between the models' learned representations and the inherent complexity of 3D scenes. This discrepancy largely stems from the training of MLLMs on predominantly 2D data, which restricts their effectiveness in comprehending 3D spaces. To address this issue, in this paper, we propose a novel generalist model, i.e., Video-3D LLM, for 3D scene understanding. By treating 3D scenes as dynamic videos and incorporating 3D position encoding into these representations, our Video-3D LLM aligns video representations with real-world spatial contexts more accurately. Additionally, we have implemented a maximum coverage sampling technique to optimize the balance between computational costs and performance efficiency. Extensive experiments demonstrate that our model achieves state-of-the-art performance on several 3D scene understanding benchmarks, including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
Related papers
- Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs [72.11701578308804]
This paper categorizes recent 3D Vision-Language Models into 3D object-centric, 2D image-based, and 3D scene-centric approaches.<n>Despite the architectural similarity of 3D scene-centric VLMs to their 2D counterparts, they have exhibited comparatively lower performance compared with the latest 3D object-centric and 2D image-based approaches.<n>Our investigation suggests that while these models possess cross-modal alignment capabilities, they tend to over-rely on linguistic cues and overfit to frequent answer distributions.
arXiv Detail & Related papers (2025-06-05T17:56:12Z) - MLLMs Need 3D-Aware Representation Supervision for Scene Understanding [14.083262551714133]
3DRS is a framework that enhances MLLM 3D representation learning by introducing supervision from pretrained 3D foundation models.<n>Our approach aligns MLLM visual features with rich 3D knowledge distilled from 3D models, effectively improving scene understanding.
arXiv Detail & Related papers (2025-06-02T17:58:24Z) - Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors [23.66183317100899]
Previous research has investigated the application of Multimodal Large Language Models (MLLMs) in understanding 3D scenes by interpreting them as videos.<n>We propose a novel and efficient method, the Video-3D Geometry Large Language Model (VG LLM)<n>Our approach employs a 3D visual geometry encoder that extracts 3D prior information from video sequences.
arXiv Detail & Related papers (2025-05-30T14:16:41Z) - VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction [86.82819259860186]
We introduce VLM-3R, a unified framework for Vision-Language Models (VLMs) that incorporates 3D Reconstructive instruction tuning.<n>VLM-3R processes monocular video frames by employing a geometry encoder to derive implicit 3D tokens that represent spatial understanding.
arXiv Detail & Related papers (2025-05-26T17:56:30Z) - MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation [87.30919771444117]
Reasoning segmentation aims to segment target objects in complex scenes based on human intent and spatial reasoning.
Recent multimodal large language models (MLLMs) have demonstrated impressive 2D image reasoning segmentation.
We introduce MLLM-For3D, a framework that transfers knowledge from 2D MLLMs to 3D scene understanding.
arXiv Detail & Related papers (2025-03-23T16:40:20Z) - 3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow [69.94527569577295]
3D vision and spatial reasoning have long been recognized as preferable for accurately perceiving our three-dimensional world.
Due to the difficulties in collecting high-quality 3D data, research in this area has only recently gained momentum.
We propose converting existing densely activated LLMs into mixture-of-experts (MoE) models, which have proven effective for multi-modal data processing.
arXiv Detail & Related papers (2025-01-28T04:31:19Z) - 3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene Understanding [49.15555885075644]
We develop pipeline based on open-source 2D MLLMs and LLMs to generate high-quality 3D-text pairs.
We introduce the 3UR-LLM model, an end-to-end 3D MLLM designed for precise interpretation of 3D scenes.
arXiv Detail & Related papers (2025-01-14T03:50:23Z) - 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) - EmbodiedSAM: Online Segment Any 3D Thing in Real Time [61.2321497708998]
Embodied tasks require the agent to fully understand 3D scenes simultaneously with its exploration.
An online, real-time, fine-grained and highly-generalized 3D perception model is desperately needed.
arXiv Detail & Related papers (2024-08-21T17:57:06Z) - LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image [72.14973729674995]
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.
arXiv Detail & Related papers (2024-08-14T10:00:16Z) - Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model [51.83436609094658]
We introduce Coarse Correspondences, a simple lightweight method that enhances MLLMs' spatial-temporal reasoning with 2D images as input.
Our method uses a lightweight tracking model to identify primary object correspondences between frames in a video or across different image viewpoints.
We demonstrate that this simple training-free approach brings substantial gains to GPT4-V/O consistently on four benchmarks.
arXiv Detail & Related papers (2024-08-01T17:57:12Z) - 3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding [12.823274886850697]
We introduce a novel and efficient prompt tuning paradigm, 3DMIT.
This paradigm eliminates the alignment stage between 3D scenes and language and extends the instruction prompt with the 3D modality information.
We evaluate the effectiveness of our method across diverse tasks in the 3D scene domain.
arXiv Detail & Related papers (2024-01-06T12:20:18Z) - Chat-3D: Data-efficiently Tuning Large Language Model for Universal
Dialogue of 3D Scenes [56.727745047799246]
3D scene understanding has gained significant attention due to its wide range of applications.
This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs.
arXiv Detail & Related papers (2023-08-17T03:52:15Z) - 3D-LLM: Injecting the 3D World into Large Language Models [60.43823088804661]
Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning.
We propose to inject the 3D world into large language models and introduce a 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.
arXiv Detail & Related papers (2023-07-24T17:59:02Z) - Multi-CLIP: Contrastive Vision-Language Pre-training for Question
Answering tasks in 3D Scenes [68.61199623705096]
Training models to apply common-sense linguistic knowledge and visual concepts from 2D images to 3D scene understanding is a promising direction that researchers have only recently started to explore.
We propose a novel 3D pre-training Vision-Language method, namely Multi-CLIP, that enables a model to learn language-grounded and transferable 3D scene point cloud representations.
arXiv Detail & Related papers (2023-06-04T11:08:53Z)
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.