{"total":48,"rows":[{"id":"2402.17766","title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","created":"2024-02-27","cited_by_count":137,"area":"mllm"},{"id":"2207.11514","title":"Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models","created":"2022-07-23","cited_by_count":95,"area":"mllm"},{"id":"2303.11313","title":"CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition","created":"2023-03-20","cited_by_count":77,"area":"mllm"},{"id":"2503.13111","title":"MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs","created":"2025-03-17","cited_by_count":73,"area":"mllm"},{"id":"2405.10255","title":"When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models","created":"2024-05-16","cited_by_count":43,"area":"mllm"},{"id":"2311.17618","title":"ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model","created":"2023-11-29","cited_by_count":39,"area":"mllm"},{"id":"2411.16856","title":"SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE","created":"2024-11-25","cited_by_count":39,"area":"mllm"},{"id":"2506.01853","title":"ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding","created":"2025-06-02","cited_by_count":38,"area":"mllm"},{"id":"2501.02765","title":"Visual Large Language Models for Generalized and Specialized Applications","created":"2025-01-06","cited_by_count":36,"area":"mllm"},{"id":"2312.04016","title":"PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation","created":"2023-12-07","cited_by_count":28,"area":"mllm"},{"id":"2401.06129","title":"Distilling Vision-Language Models on Millions of Videos","created":"2024-01-11","cited_by_count":20,"area":"mllm"},{"id":"2412.17759","title":"Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy","created":"2024-12-23","cited_by_count":17,"area":"mllm"},{"id":"2407.09781","title":"Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding","created":"2024-07-13","cited_by_count":16,"area":"mllm"},{"id":"2308.02982","title":"Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation","created":"2023-08-06","cited_by_count":12,"area":"mllm"},{"id":"2306.08226","title":"CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration","created":"2023-06-14","cited_by_count":9,"area":"mllm"},{"id":"2404.16538","title":"OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images","created":"2024-04-25","cited_by_count":9,"area":"mllm"},{"id":"2405.18937","title":"Kestrel: 3D Multimodal LLM for Part-Aware Grounded Description","created":"2024-05-29","cited_by_count":9,"area":"mllm"},{"id":"2503.10529","title":"PiSA: A Self-Augmented Data Engine and Training Strategy for 3D Understanding with Large Models","created":"2025-03-13","cited_by_count":8,"area":"mllm"},{"id":"2406.10740","title":"FreeMotion: MoCap-Free Human Motion Synthesis with Multimodal Large Language Models","created":"2024-06-15","cited_by_count":6,"area":"mllm"},{"id":"2406.11840","title":"LLaNA: Large Language and NeRF Assistant","created":"2024-06-17","cited_by_count":6,"area":"mllm"},{"id":"2506.07575","title":"Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Models","created":"2025-06-09","cited_by_count":5,"area":"mllm"},{"id":"2506.09935","title":"LEO-VL: Efficient Scene Representation for Scalable 3D Vision-Language Learning","created":"2025-06-11","cited_by_count":5,"area":"mllm"},{"id":"2606.14699","title":"Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control","created":"2026-06-12","cited_by_count":4,"area":"mllm"},{"id":"2404.10146","title":"Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels","created":"2024-04-15","cited_by_count":3,"area":"mllm"},{"id":"2605.16745","title":"EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers","created":"2026-05-16","cited_by_count":3,"area":"mllm"},{"id":"2404.14678","title":"3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset","created":"2024-04-23","cited_by_count":2,"area":"mllm"},{"id":"2505.02325","title":"TeDA: Boosting Vision-Lanuage Models for Zero-Shot 3D Object Retrieval via Testing-time Distribution Alignment","created":"2025-05-05","cited_by_count":2,"area":"mllm"},{"id":"2603.09173","title":"Point Cloud as a Foreign Language for Multi-modal Large Language Model","created":"2026-03-10","cited_by_count":2,"area":"mllm"},{"id":"2208.14649","title":"DetailCLIP: Injecting Image Details into CLIP's Feature Space","created":"2022-08-31","cited_by_count":1,"area":"mllm"},{"id":"2308.10185","title":"ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights","created":"2023-08-20","cited_by_count":1,"area":"mllm"},{"id":"2407.00905","title":"Learning Robust 3D Representation from CLIP via Dual Denoising","created":"2024-07-01","cited_by_count":1,"area":"mllm"},{"id":"2505.18819","title":"Parameter-Efficient CLIP Adaptation for 3D Understanding via Unified Tokenization","created":"2025-05-24","cited_by_count":1,"area":"mllm"},{"id":"2507.20110","title":"NeuroVoxel-LM: Language-Aligned 3D Perception via Dynamic Voxelization and Meta-Embedding","created":"2025-07-27","cited_by_count":1,"area":"mllm"},{"id":"2511.15311","title":"Adapt-As-You-Walk Through the Clouds: Training-Free Online Test-Time Adaptation of 3D Vision-Language Foundation Models","created":"2025-11-19","cited_by_count":1,"area":"mllm"},{"id":"2602.20409","title":"CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation","created":"2026-02-23","cited_by_count":1,"area":"mllm"},{"id":"2605.10887","title":"Count Anything at Any Granularity","created":"2026-05-11","cited_by_count":1,"area":"mllm"},{"id":"2606.06485","title":"PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding","created":"2026-06-04","cited_by_count":1,"area":"mllm"},{"id":"2608.29285","title":"3D-MRL: Nested Multimodal 3D Representations via Matryoshka Representation Learning","created":"2026-08-29","cited_by_count":1,"area":"mllm"},{"id":"2312.17051","title":"FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models","created":"2023-12-28","cited_by_count":0,"area":"mllm"},{"id":"2501.03879","title":"CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds","created":"2025-01-07","cited_by_count":0,"area":"mllm"},{"id":"2504.13995","title":"Scaling LLaNA: Advancing NeRF-Language Understanding Through Large-Scale Training","created":"2025-04-18","cited_by_count":0,"area":"mllm"},{"id":"2506.22375","title":"Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation","created":"2025-06-27","cited_by_count":0,"area":"mllm"},{"id":"2507.08513","title":"Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation","created":"2025-07-11","cited_by_count":0,"area":"mllm"},{"id":"2604.15703","title":"P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models","created":"2026-04-17","cited_by_count":0,"area":"mllm"},{"id":"2606.18472","title":"Domain Generalizable Adaptation of 3D Vision-Language Models via Regularized Fine-Tuning","created":"2026-06-16","cited_by_count":0,"area":"mllm"},{"id":"2608.05539","title":"OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction","created":"2026-08-06","cited_by_count":0,"area":"mllm"},{"id":"2608.25876","title":"Do Vision-Language Models Agree on the Affective Qualities of Shape? A Cross-Model Audit for Generative Design Interfaces","created":"2026-08-26","cited_by_count":0,"area":"mllm"},{"id":"2609.00845","title":"Towards Generalizable Visually Grounded Exploration of Household Devices","created":"2026-09-01","cited_by_count":0,"area":"mllm"}],"next_cursor":null}