Efficient3D: A Unified Framework for Adaptive and Debiased Token Reduction in 3D MLLMs

Researchers have developed Efficient3D, a framework that accelerates 3D Multimodal Large Language Models (MLLMs) while maintaining accuracy through adaptive token pruning. The system uses a Debiased Visual Token Importance Estimator and Adaptive Token Rebalancing to reduce computational overhead without sacrificing performance, showing +2.57% CIDEr improvement on benchmarks.

• →Efficient3D addresses the high computational overhead of 3D MLLMs that limits their deployment on resource-constrained platforms.
• →The framework introduces DVTIE module for more reliable importance predictions and ATR strategy for dynamic pruning adjustment based on scene complexity.
• →Testing on five 3D vision and language benchmarks showed superior performance with a +2.57% CIDEr improvement on Scan2Cap dataset.
• →The solution enables context-aware token reduction while maintaining essential semantics with lower computation requirements.
• →Code has been released open-source, making the framework accessible for broader research and implementation.