GST-VLA: Structured Gaussian Spatial Tokens for 3D Depth-Aware Vision-Language-Action Models
GST-VLA: Structured Gaussian Spatial Tokens for 3D Depth-Aware Vision-Language-Action Models explores GST-VLA introduces a novel Gaussian Spatial Tokenizer for enhanced 3D depth-aware vision-language-action modeling.. Commercial viability score: 3/10 in 3D Vision-Language Models.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkLook Before Acting: Enhancing Vision Foundation Representations for Vision-Language-Action ModelsViability: 3.0
- Higher Viability3D-Mix for VLA: A Plug-and-Play Module for Integrating VGGT-based 3D Information into Vision-Language-Action ModelsViability: 7.0
- Higher ViabilityAugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action ModelsViability: 7.0
- Higher ViabilityDepthCache: Depth-Guided Training-Free Visual Token Merging for Vision-Language-Action Model InferenceViability: 8.0
- Higher Viability