AdaToken-3D: Dynamic Spatial Gating for Efficient 3D Large Multimodal-Models Reasoning

TL;DR

AdaToken-3D introduces a dynamic spatial token pruning method that significantly improves the efficiency of large 3D multimodal models by reducing computational costs while preserving accuracy.

Contribution

This work presents a novel adaptive spatial token pruning framework for 3D multimodal models, systematically analyzing redundancy patterns and enhancing inference efficiency.

Findings

Achieves 21% faster inference speed

Reduces 63% FLOPs without accuracy loss

Over 60% of spatial tokens are minimally contributive

Abstract

Large Multimodal Models (LMMs) have become a pivotal research focus in deep learning, demonstrating remarkable capabilities in 3D scene understanding. However, current 3D LMMs employing thousands of spatial tokens for multimodal reasoning suffer from critical inefficiencies: excessive computational overhead and redundant information flows. Unlike 2D VLMs processing single images, 3D LMMs exhibit inherent architectural redundancy due to the heterogeneous mechanisms between spatial tokens and visual tokens. To address this challenge, we propose AdaToken-3D, an adaptive spatial token optimization framework that dynamically prunes redundant tokens through spatial contribution analysis. Our method automatically tailors pruning strategies to different 3D LMM architectures by quantifying token-level information flows via attention pattern mining. Extensive experiments on LLaVA-3D (a 7B parameter 3D-LMM) demonstrate that AdaToken-3D achieves 21\% faster inference speed and 63\% FLOPs reduction while maintaining original task accuracy. Beyond efficiency gains, this work systematically investigates redundancy patterns in multimodal spatial information flows through quantitative token interaction analysis. Our findings reveal that over 60\% of spatial tokens contribute minimally ($<$5\%) to the final predictions, establishing theoretical foundations for efficient 3D multimodal learning.