OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs

arXiv:2606.22617 · cs.CV · Submitted 2026-06-21 · Read on arXiv

cs.CV

Submitted: 2026-06-21

Updated: 2026-09-26

Terminology

• Benchmark — 18× in this paper · explained in 2 episodes
• Distillation — 17× in this paper · explained in 2 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Foundation Model — 7× in this paper · explained in 3 episodes
• Autonomous Vehicles — 6× in this paper
• Adaptation — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Multimodal Dataset — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Active Exploration — 1× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• In International Conference on Learning Representations — 1× in this paper
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• Large Language Model-assisted Autonomous Vehicle Recovery from Immobilization
• Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
• ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation
• Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenes
• VDRive: Leveraging Reinforced VLA and Diffusion Policy for End-to-end Autonomous Driving
• 3DRS: MLLMs Need 3D-Aware Representation Supervision for Scene Understanding
• GPT-4o System Card
• EMMA: End-to-End Multimodal Model for Autonomous Driving
• Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
• LLaVA-OneVision: Easy Visual Task Transfer
• SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
• End-to-End Driving with Online Trajectory Evaluation via BEV World Model
• X-Driver: Explainable Autonomous Driving with Vision-Language Models
• LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving
• One Million Scenes for Autonomous Driving: ONCE Dataset
• HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
• Gemma: Open Models Based on Gemini Research and Technology

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