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3D Object Detection Evolves: Vision-Language Models Revolutionize Spatial Understanding

This is a Plain English Papers summary of a research paper called 3D Object Detection Evolves: Vision-Language Models Revolutionize Spatial Understanding . If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter . Introduction: The Evolution from 2D to 3D Object Detection with Language Understanding Object detection has evolved significantly, transitioning from traditional 2D methods to more sophisticated 3D approaches. While 2D object detection identifies objects solely within flat image coordinates, 3D detection captures the full spatial context by incorporating depth information. This advancement addresses the fundamental limitation of 2D methods—their inability to represent an object's volume, size, and spatial relationships. Traditional object detection systems emerged as a cornerstone of computer vision, supporting applications from autonomous vehicles to surveillance. Early approaches used handcrafted features, but deep learning breakthroughs led to powerful real-time detectors like YOLO, SSD, and Faster R-CNN. Despite their accuracy, these 2D systems cannot handle tasks requiring spatial reasoning in three-dimensional environments. Figure 1: Illustration of VLM-based 3D detection showing key advantages over traditional methods, including semantic awareness, zero-shot capabilities, and human-aligned queries. To overcome these spatial limitations, the field shifted toward 3D object detection methods using volumetric data like LiDAR point clouds and depth maps. While these traditional 3D approaches improved spatial reasoning, they introduced new challenges. They require extensive manual annotations, struggle with cross-domain generalization, lack semantic flexibility, and need rigid retraining processes to adapt to new object categories. Additionally, they typically depend on carefully calibrated sensor systems and lack interpretability. Vision-Language Models (VLMs) offer a transformative solution to these challenges by combining visual perception with natural language understanding. As shown in Figure 1, VLMs inject semantic reasoning into 3D object detection, enabling five key advantages over traditional methods: prompt-based control, zero-shot generalization, semantic awareness, multimodal integration, and interpretability. Unlike CNN-based systems, VLMs can process natural language queries and integrate high-level reasoning into the detection process, allowing for more intuitive interactions like "find the nearest ripe apple." This language-guided approach to 3D perception represents a paradigm shift in how machines understand and interact with the physical world. Comprehensive Research Methodology: A Hybrid Academic and AI-Powered Approach Search Strategy Figure 4: Diagram showing the paper selection methodology, combining traditional academic database searches with AI-powered search tools to identify the most relevant literature. This review employed a novel dual-strategy approach that combines traditional academic databases with AI-powered search engines—the first such effort in this domain. The researchers queried twelve prominent platforms, including academic databases (Google Scholar, IEEE Xplore, Scopus, arXiv, ScienceDirect, PubMed, Web of Science) and AI-based engines (Hugging Face, ChatGPT, DeepSeek, Grok, Perplexity). Core search terms included combinations like "3D Object Detection," "Vision Language Models," "Large Vision Language Models," "VLMs," "LLMs," and "Robotics." Queries such as "3D Object Detection + VLMs + Robotics" were designed to span traditional deep learning paradigms and their extension into multimodal perception systems. The initial search yielded 459 papers. A three-stage filtering process based on relevance, methodological soundness, and novelty narrowed this to a final selection of 105 papers—43 on traditional neural network-based 3D object detection and 62 addressing Vision-Language Models approaches. Literature Evaluation and Inclusion Criteria Each selected paper was evaluated using four key criteria: (1) clear methodological contribution to 3D object detection, (2) empirical validation or real-world demonstration, (3) architectural or computational novelty, and (4) relevance to robotics, automation, or agricultural contexts. The researchers paid particular attention to papers using VLMs or multimodal transformers to enhance 3D spatial understanding. They also included several foundational works from 2017–2021 (e.g., PointNet++, VoteNet, SECOND, PV-RCNN, 3DSSD) as comparative baselines for traditional neural network methods. This approach helped ground the discussion of seven limitations in conventional 3D detection systems—Annotation-Heavy, Poor Generalization, No Semantics, Rigid Training, Sensor Dependency, No Prompting, and Limited Flexibility—compared to more recent VLM-powered approaches. Synthesis and Comparative Analysis Figure 3: Conceptual framework showing the analytical approach used in the review, progressing from comparison of methods through architecture analysis to future directions. The review synthesizes the current state of 3D object detection through the lens of VLMs, following a structured approach from literature selection to architectural and application-based evaluations. Results are organized to show the evolution from traditional deep learning approaches to modern VLM-based systems. The analysis reveals how traditional methods—such as PointNet++, VoteNet, and PV-RCNN—primarily rely on geometric features from point clouds or voxelized data. These methods offer high accuracy in structured environments but face limitations in semantic understanding and generalization. In contrast, VLM-based approaches like DetGPT-3D, ContextDET, and task-conditioned LVLMs integrate textual prompts with visual-spatial inputs, enabling open-vocabulary detection, instruction following, and zero-shot generalization. These systems show particular promise in robotics applications requiring nuanced reasoning, especially in unstructured settings. This comparison of visual-language models for transportation applications highlights how architecture-focused analysis reveals the foundational components of VLMs for 3D detection, including multimodal fusion layers, pretraining strategies, and fine-tuning mechanisms. The comparative framework evaluates both approaches across key criteria—annotation dependence, interpretability, data efficiency, and computational cost. The Technical Landscape of 3D Detection with VLMs Evolution of 3D Object Detection Approaches Traditional Methods: Foundational Geometric Approaches to 3D Detection 3D object detection emerged as a significant research area with the increasing availability of depth sensors and LiDAR technology in the early 2010s. Initially driven by robotics and autonomous vehicle applications, the field gained momentum with datasets like KITTI in 2012, which provided annotated LiDAR and camera data for benchmarking. Early methods relied heavily on hand-crafted features and geometric reasoning. The field experienced rapid advancement around 2015 with the rise of deep learning, particularly CNNs adapted for processing point clouds and voxelized data. Architectures like VoxelNet (2017) and PointNet/PointNet++ (2017) marked turning points by directly learning from raw 3D data without complex preprocessing. These innovations enabled more accurate and scalable 3D detection systems, fueling research in autonomous driving, augmented reality, and robotic manipulation. The growing demand for spatial awareness in real-world scenarios continues to drive advancements, making 3D object detection critical for intelligent systems and multimodal AI. Model (Release Date) & Reference Type Processing Method VoxelNet (Zhou and Tuzel, 2018) Non-Attention Voxel-wise PointNet (Qi et al., 2017) Non-Attention Point-wise MV3D (Chen et al., 2017) Non-Attention ROI-wise (Multi-view Fusion) SECOND (Yan et al., 2018) Non-Attention Voxel-wise Frustum PointNet (Qi et al., 2018) Non-Attention Point-wise (RGB-D Frustum Proposal) MV3D (Chen et al., 2017) Non-Attention ROI-wise AVOD (Ku et al., 2018) Non-Attention ROI-wise PointFusion (Xu et al., 2018) Non-Attention Point-wise MVX-Net (Sindagi et al., 2019) Attention-Based Voxel-wise PointPainting (Vora et al., 2020) Attention-Based Point-wise 3D-CVF (Yoo et al., 2020) Attention-Based Point-wise EPNet (Huang et al., 2020) Attention-Based Point-wise LiDAR-RCNN (Li et al., 2021b) Non-Attention ROI-wise (LiDAR-Camera Fusion) DepthFusionNet (Shivakumar et al., 2019) Non-Attention Voxel-wise (Depth-Guided Fusion) PointAugment (Li et al., 2020) Attention-Based Point-wise (Data Augmentation) Stereo3DNet (Chen et al., 2020) Attention-Based Monocular (Stereo Vision) SVGA Net VoxelGraph (He et al., 2022) Non-Attention Voxel-wise (Graph Neural Networks) Table 1: Timeline and classification of Traditional Methods 3D object detection (with convolution neural networks), showing the evolution from non-attention to attention-based approaches with various processing methods. Vision-Language Models: Bridging 3D Perception with Semantic Understanding Vision-Language Models (VLMs) represent a significant advancement in 3D object detection by integrating language understanding with visual perception. Unlike traditional approaches that rely solely on geometric features, VLMs leverage the semantic richness of language to enhance spatial reasoning and object recognition in 3D environments. Key VLM models like PaLM-E, LLaVA-1.5, and 3D-LLM have introduced novel architectural innovations that allow for cross-modal understanding. These systems can process natural language instructions alongside visual data, enabling more intuitive and flexible detection capabilities. For example, users can specify objects using natural language descriptions rather than predefined categories, greatly expanding the utility of 3D detection systems. The transition from purely geometric reasoning to language-guided perception represents a paradigm shift in how machines interpret 3D scenes. By incorporating text, VLMs can understand context, relationships between objects, and even intent, making them particularly valuable for interactive applications like robotics and augmented reality. Model (Release Date) & Reference Type Processing Method PaLM-E [Driess et al. 2023] LLaVA-1.5 [Zhu et al. 2024] BLIP-2 [Li et al. 2023a] InternVL [Zhu et al. 2025] CogVLM [Tian et al. 2024; Wang et al. 2024d] CLIP3D-Det [Hegde et al. 2023] Instruct3D [Kamata et al. 2023] M3D-Labled [Bai et al. 2024] Attention-Based Attention-Based Encoder-Decoder Encoder-Decoder Attention-Based Attention-Based Encoder-Decoder Embodied 3D Perception Multimodal Visual Grounding Image-Text 3D Localization Visual-Language Spatial Reasoning Vision-Language Spatial Alignment Qwen2-VL [Wang et al. 2024a] Find n' Propagate [Echegaray et al. 2024] $\begin{aligned} & \text { Attention-Based } \ & \text { Attention-Based } \ & \text { Transformer + CLIP } \end{aligned}$ Monocular (CLIP Feature Fusion) ROI-wise (Instruction Tuning) 3D Medical Image Understanding via Multi-modal Instruction Tuning OWL-ViT Link to paper Decoder-Only Hybrid Top- down/Bottom-up Open-Vocabulary 3D Detection using Frustum Search, Cross-Modal Propag- tion, and Remote Simulation Table 2: Timeline and classification of 3D object detection with Vision-Language Models, showing various approaches from attention-based mechanisms to encoder-decoder architectures for different processing tasks. This exploration of frontier vision-language models highlights how VLMs have developed from specialized tools to versatile systems capable of understanding complex spatial relationships through language guidance. Technical Foundations: How VLMs Understand and Detect 3D Objects Pretraining and Fine-tuning Architecture: Building Cross-Modal Understanding Figure 6: Overview of VLM architecture showing the pretraining and fine-tuning phases, illustrating how visual embeddings are projected into a shared space with text tokens for multimodal understanding. The architectural pipeline of VLMs follows a sequential design with distinct pretraining and fine-tuning phases. During pretraining, the model learns cross-modal alignment through a three-stage framework: An image encoder (e.g., CLIP-ViT) processes input images into visual embeddings A multimodal projector (e.g., dense neural networks) maps these embeddings into the text token space A text decoder (e.g., Vicuna) generates captions or answers autoregressively The core technical challenge lies in unifying image and text representations. Visual tokens are projected into the decoder's embedding space and concatenated with text tokens, forming a fused input sequence. For example, LLaVA employs a CLIP-based encoder to extract image features, which are linearly projected into Vicuna's token dimension. These projected visual tokens are prepended to text embeddings, enabling the decoder to process multimodal inputs seamlessly. Pretraining strategies typically involve two stages. First, the multimodal projector is trained to align visual and textual features while keeping the image encoder and text decoder frozen. The projector is optimized using cross-entropy loss to align visual embeddings with the decoder's expected text space. In the second stage, both the projector and decoder are jointly fine-tuned on task-specific data, refining cross-modal reasoning. During fine-tuning, the pretrained image encoder and projector remain fixed or lightly updated, while the text decoder is adapted to downstream tasks. For detection or grounding, textual instructions are concatenated with projected visual tokens, conditioning the decoder to generate structured outputs like coordinates or masks. Key innovations include lightweight projectors for parameter efficiency and spatial-aware architectures that preserve pixel-level details for localization tasks. The modular design—freezing encoders, training projectors, and adapting decoders—enables VLMs to scale across diverse applications, from captioning to 3D detection, while maintaining robust zero-shot capabilities. From 2D to 3D Detection: Extending VLMs to Spatial Understanding Figure 7: Comparison of 2D and 3D object detection in an orchard setting, showing how 2D detections are projected into 3D space and refined with depth information. VLMs detect objects in 2D through a multimodal process combining visual feature extraction and semantic alignment (Fig. 7). First, an image encoder processes the input image into spatial embeddings, capturing hierarchical features. These embeddings are projected into a joint vision-language space via a multimodal projector, aligning them with textual token embeddings. The text decoder then interprets these fused representations to generate bounding boxes or segmentation masks. Three primary strategies govern this process: Zero-Shot Prediction : The model matches visual features to textual labels without fine-tuning, enabling detection of novel categories not seen during training. Fine-Tuned Detection : The model is adapted to specific detection tasks through supervised learning on annotated datasets, preserving language-vision alignment while improving localization accuracy. Instruction-Tuned Approach : The model is conditioned on natural language instructions that specify detection goals, allowing flexible, context-aware object identification based on user queries. Extending from 2D to 3D detection, VLMs must incorporate depth information and spatial context. This transition involves several approaches: Depth Projection : 2D detections are projected into 3D space using depth maps or LiDAR data, creating volumetric bounding boxes. 3D-Aware Processing : Native 3D models directly process point clouds or voxel grids with language conditioning. Cross-Modal Fusion : Multiple sensing modalities (RGB, depth, LiDAR) are fused with textual guidance for robust spatial understanding. These techniques allow VLMs to perform tasks like unlocking textual-visual understanding for open-vocabulary 3D detection , where language queries guide the discovery and classification of objects in 3D scenes even when they were not explicitly trained on those categories. Comparative Analysis: Strengths and Limitations of VLM-Based 3D Detection Recent advances in 3D object detection via VLMs have shown impressive potential, particularly in zero-shot and open-vocabulary scenarios. Unlike traditional 3D detectors that depend heavily on large-scale annotated datasets, modern VLM-based approaches harness pretrained 2D vision-language priors (e.g., CLIP, GPT-3/4) to bridge the modality gap between images, point clouds, and language. Notable approaches include: PointCLIP V2 : Demonstrates how realistic projection modules and GPT-generated prompts can achieve strong 3D performance without any 3D training data. 3DVLP : Leverages object-level contrastive learning to boost transferability across tasks such as detection, grounding, and captioning. Language-free 3DVG : Reduces reliance on human annotations by synthesizing pseudo-language features from multi-view images, achieving effective grounding without textual supervision. Uni3DL : Focuses on architectural unification and cross-modal alignment to enhance scalability and generalization in real-world 3D scenes. However, several challenges remain. Many methods rely on complex training pipelines, increasing computational cost and limiting real-time applicability. Others face generalization issues when exposed to sparse data, unfamiliar object categories, or diverse environments. Performance can be sensitive to prompt design, quality of multi-view imagery, or LiDAR calibration accuracy. Author (Year) 3D Object Detection Method Key Contribution and Strengths Drawbacks / Limitations [Chen et al. 2024d] SpatialVLM (2024) Spatial VQA using internetscale spatial data with visionlanguage pretraining Introduced large-scale spatial QA dataset enabling quantitative and qualitative 3D reasoning from 2D images Limited to synthetic QA data, relies heavily on monocular depth, potential bias in templated questions [Hong et al. 2023] 3D-LLM (2023) Injecting 3D point clouds into large language models for holistic 3D understanding Proposed a new family of 3D-LLMs that perform diverse 3D tasks using 3D-language data; uses multi-view rendered features aligned with 2D VLMs and a 3D localization mechanism for better spatial reasoning Requires 3D rendering and feature extraction pipeline; training depends on alignment with 2D backbones; limited availability of largescale 3D-language data compared to image-language datasets [Fruhwirth-Reisinger et al. 2024] Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection (2024) Vision-language-guided unsupervised 3D detection using LiDAR point clouds with CLIP for classifying point clusters Introduced a vision-language-guided approach to detect both static and moving objects in LiDAR point clouds using CLIP, achieving state-of-the-art results on Waymo and Argoverse 2 datasets ( +23 AP3D and +7.9 AP3D) Relies on multi-view projections and temporal refinement, requires large-scale LiDAR datasets, and may face difficulties with distant and incomplete objects due to LiDAR's inherent limitations [Jiao et al. 2024] Unlocking Textual and Visual Wisdom: OpenVocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image (2024) Open-vocabulary 3D object detection enhanced by visionlanguage model guidance for novel object discovery in 3D scenes Introduced a hierarchical alignment approach using vision-language models to discover novel classes in 3D scenes, showing significant improvements in accuracy and generalization in real-world scenarios Relies on pretrained VLMs and object detection models, limited by availability of 3D scene data for training and generalization to unseen categories in certain complex environments Comparison of VLM-based 3D object detection methods, highlighting strengths, innovations, and key limitations. Aspect Traditional 3D Neural Networks (e.g., PointNet, VoxelNet, DeepFusion) VLM-Based 3D Object Detection (e.g., 3D-LLM, OV-SCAN, PaLM-E, LLaVA, CogVLM) Modality Usage Primarily use LiDAR point clouds or fused 2D/3D inputs (RGB-D, voxel grids). Combine point clouds, images, and natural language through multimodal fusion. Object Vocabulary Limited to closed-set categories defined by training datasets. Enable open-vocabulary detection by aligning text and visual features. Learning Paradigm Supervised learning with task-specific datasets. Often zero-shot or few-shot learning via pre-trained VLMs. Scene Understanding Focused on geometric/spatial features; less semantic context. Rich semantic understanding via language grounding and visual prompts. Annotation Dependency Heavy reliance on dense 3D labels, expensive to annotate. Leverages pre-trained models, enabling weakly supervised learning. Explainability Limited interpretability; primarily geometric. Offers natural language explanations, captions, and scene descriptions. Computational Complexity Relatively efficient, optimized for real-time. Higher complexity due to large languagevision backbones. Generalization Poor generalization to unseen categories or new domains. Strong zero-shot generalization across tasks and categories. Data Efficiency Requires large labeled datasets. Pretraining enables higher data efficiency. Use Cases Primarily used in structured scenarios like autonomous driving. Extends to instruction following, navigation, and 3D QA. Integration with Language Minimal to none. Tightly integrated, allowing language queries or dialogue-based tasks. Table 3: Comparison of traditional CNN-based and VLM-based 3D object detection highlights key trade-offs: while conventional methods rely on dense 3D supervision and fixed categories, VLMs enable open-vocabulary, zero-shot reasoning by leveraging language and vision pretraining. VLMs offer richer semantic understanding and flexibility, though often at higher computational cost, making them ideal for broader, instruction-driven 3D tasks. Advantages Traditional 3D NN Methods VLM-Based 3D Methods Speed & Simplicity ✓ Simpler and faster inference ✗ Heavier and slower Semantic Richness ✗ Label-limited ✓ Language- grounded Open-Vocabulary Sup- port ✗ Fixed class set ✓ Detects novel classes Annotation Cost ✗ High manual ef- fort ✓ Uses pretraining Real-Time Deployment ✓ Optimized imple- mentations ✗ Computationally heavy Generalization ✗ Domain-specific ✓ Strong zero-shot capability Table 4: Trade-offs between Traditional and VLM-Based 3D Object Detection, showing complementary strengths in different operational domains. This comprehensive review of 3D object detection with VLMs demonstrates how the field is actively addressing these challenges through innovative architectural designs and training strategies. Challenges and Future Directions for 3D Detection with VLMs Current Technical Limitations: Identifying Barriers to Adoption Despite rapid advancements, VLMs for 3D object detection face several unresolved challenges that limit their real-world applicability. Unlike traditional geometry-driven detectors, VLMs often exhibit subpar spatial reasoning capabilities and struggle with accurate depth estimation and object localization in cluttered environments. The most pressing technical bottlenecks include: Spatial Reasoning Limitations : VLMs struggle with 3D spatial interpretation, often misidentifying relative positions and object orientations. Their depth understanding is limited, leading to inaccuracies in bounding box placement. Cross-Modal Misalignment : Projecting high-dimensional 3D features into language embedding spaces causes loss of geometric detail. This weakens performance on occluded or structurally complex scenes. High Annotation Overhead : VLMs demand richly annotated 3D-text datasets, which are expensive to produce. Manual alignment hinders scalability. Limited Real-Time Viability : Transformer-heavy VLMs operate at low frame rates (8–15 FPS), contrasting with efficient voxel-based detectors that exceed 50 FPS. Vulnerability to Occlusion : Without depth priors, VLMs often fail to localize partially obscured objects, whereas traditional LiDAR-based methods handle such cases more robustly. Weak Domain Generalization : VLMs overfit to training domains and underperform under domain shifts (e.g., lighting, sensor variance), unlike more stable geometry-driven models. Semantic Hallucinations : Prompt-driven VLMs can infer incorrect detections when text inputs are ambiguous, reducing reliability in safety-critical applications. Lack of Explicit 3D Structure : VLMs frequently omit explicit 3D modeling, resulting in multiview inconsistencies and poor spatial coherence across frames. Promising Solutions and Research Directions: Overcoming Current Limitations Addressing the identified limitations in VLM-based 3D object detection requires innovative strategies combining advances in spatial modeling, multimodal alignment, data efficiency, and computational optimization. Promising solutions include: Improved Spatial Reasoning : Integrating 3D scene graphs and geometric priors for more accurate spatial reasoning. Models like SpatialRGPT demonstrate how structured spatial representations can enhance VLM understanding of 3D environments. Advanced Multimodal Alignment : Introducing modality-specific encoders to improve cross-modal consistency. Techniques like hyperbolic mapping and contrastive learning help preserve geometric details when aligning visual and language representations. Synthetic Data Solutions : Leveraging generative AI and large language models as scalable alternatives to manual annotation. Synthetic data pipelines can create diverse 3D scenes with detailed annotations at a fraction of the cost of manual labeling. Computational Efficiency : Reinforcement learning-based methods and region-aware planning modules show potential to bring VLM inference closer to real-time performance requirements. Robust Generalization : Training on diverse and dynamic environments to counteract prompt drift and semantic ambiguity. Techniques like progressive domain adaptation and continual learning help VLMs generalize across different operational contexts. These emerging approaches point to a future where VLMs can combine the semantic richness of language with the spatial precision needed for reliable 3D object detection in real-world scenarios. Conclusion: The Future Landscape of 3D Object Detection with VLMs This review systematically examined the trajectory and current landscape of 3D object detection with Vision-Language Models, providing a comprehensive synthesis of their evolution, methodology, and application in multimodal perception. Through a rigorous paper collection process combining academic databases and modern AI search engines, the researchers curated 105 high-quality papers spanning traditional approaches and state-of-the-art VLM-based systems. The synthesis mapped the evolution from traditional geometric and point-based methods like PointNet++, PV-RCNN, and VoteNet to newer architectures leveraging VLMs. While earlier models established foundational practices in processing LiDAR data and point clouds, they lacked the semantic abstraction required for reasoning in complex environments. VLM-based approaches overcome these limitations by fusing visual and textual modalities, enabling language-conditioned perception with zero-shot reasoning, improved adaptability, and instruction-driven task completion. The technical foundations section detailed the pretraining and fine-tuning processes that bridge 2D vision-language datasets with 3D perception tasks. Notable advancements include spatial tokenization, cross-modal transformers, and point-text alignment strategies. The review also highlighted how visualization frameworks in these models allow better interpretability through textual queries. Comparative analysis revealed that while traditional methods remain more efficient and interpretable in structured environments, VLMs significantly outperform them in open-vocabulary and dynamically changing contexts. Major current limitations include weak spatial reasoning, poor real-time performance, semantic hallucinations, and domain generalization gaps, with promising solutions like 3D scene graphs, lightweight transformers, and multimodal reward shaping indicating strong directions for future improvements. Key takeaways include: Semantic Evolution : The transition from geometry-only to multimodal systems marks a critical paradigm shift, enabling instruction-based and zero-shot understanding of spatial environments. Technical Innovation : Architectural designs like spatial reasoning modules, hyperbolic alignment losses, and pretraining-finetuning pipelines demonstrate the role of language grounding in enhancing 3D spatial cognition. Performance Trade-offs : VLMs deliver state-of-the-art semantic accuracy and generalization, yet still lag behind traditional voxel-based systems in real-time inference speed (15–20% lower FPS). Challenges and Future Directions : Solutions leveraging 3D scene graph distillation, synthetic captioning, and reinforcement learning show promise, with models like RoboFlamingo-Plus and MetaSpatial already exploring these techniques. Deployment Readiness : With advancements in neuromorphic hardware and efficient cross-modal attention mechanisms, VLMs are increasingly viable for deployment in autonomous navigation, industrial robotics, and AR-based interaction systems. By integrating language, vision, and 3D geometry, these systems offer new capabilities for intelligent interaction with the physical world. This review not only maps the state-of-the-art but also charts a course for future exploration, paving the way for robust, scalable, and interpretable VLM-based 3D perception systems that are foundational to next-generation robotics and AI. Click here to read the full summary of this paper

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