A Review of 3D Object Detection with Vision-Language Models

2025-04-25

AI Summary

This work systematically investigates the core challenges of leveraging vision-language models (VLMs) for 3D object detection—namely, spatial reasoning modeling, open-vocabulary recognition, and zero-shot generalization—distinct from 2D detection. To address these, we propose the first VLM-oriented analytical framework tailored to 3D detection, encompassing architecture taxonomies for 3D-language alignment, multi-granularity prompt engineering paradigms, and a dedicated evaluation benchmark. The framework integrates CLIP, 3D large language models, point-cloud/voxel encoders, and learnable cross-modal alignment mechanisms. We comprehensively survey over 100 state-of-the-art works, clarifying technical evolution trajectories and identifying critical bottlenecks: data scarcity, prohibitive computational overhead, and insufficient geometric-semantic coupling. Our analysis points toward future directions driven by embodied intelligence and synergistic integration of multimodal foundation models.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This review provides a systematic analysis of comprehensive survey of 3D object detection with vision-language models(VLMs) , a rapidly advancing area at the intersection of 3D vision and multimodal AI. By examining over 100 research papers, we provide the first systematic analysis dedicated to 3D object detection with vision-language models. We begin by outlining the unique challenges of 3D object detection with vision-language models, emphasizing differences from 2D detection in spatial reasoning and data complexity. Traditional approaches using point clouds and voxel grids are compared to modern vision-language frameworks like CLIP and 3D LLMs, which enable open-vocabulary detection and zero-shot generalization. We review key architectures, pretraining strategies, and prompt engineering methods that align textual and 3D features for effective 3D object detection with vision-language models. Visualization examples and evaluation benchmarks are discussed to illustrate performance and behavior. Finally, we highlight current challenges, such as limited 3D-language datasets and computational demands, and propose future research directions to advance 3D object detection with vision-language models.>Object Detection, Vision-Language Models, Agents, VLMs, LLMs, AI

Problem

• Analyzing 3D object detection challenges with vision-language models
• Comparing traditional and modern methods for 3D object detection
• Addressing dataset and computational limitations in 3D-language models

Innovation

• Utilizes vision-language models for 3D detection
• Compares point clouds with modern VLMs
• Employs pretraining and prompt engineering techniques