GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) demonstrateexceptional semantic reasoning but struggle with 3D spatial perceptionwhen restricted to pure RGB inputs. Despite leveraging implicit geo-metric priors from 3D reconstruction models, image-based methods stillexhibit a notable performance gap compared to methods using explicit3D data. We argue that this gap does not arise from insufficient geo-metric priors, but from a misalignment in the training paradigm: text-dominated fine-tuning fails to activate geometric representations withinMLLMs. Existing approaches typically resort to naive feature concate-nation and optimize directly for downstream tasks without geometry-specific supervision, leading to suboptimal structural utilization. To ad-dress this limitation, we propose GAP-MLLM, a Geometry-AlignedPre-training paradigm that explicitly activates structural perception be-fore downstream adaptation. Specifically, we introduce a visual-promptedjoint task that compels the MLLMs to predict sparse pointmaps along-side semantic labels, thereby enforcing geometric awareness. Further-more, we design a multi-level progressive fusion module with a token-level gating mechanism, enabling adaptive integration of geometric priorswithout suppressing semantic reasoning. Extensive experiments demon-strate that GAP-MLLM significantly enhances geometric feature fusionand consistently enhances performance across 3D visual grounding, 3Ddense captioning, and 3D video object detection tasks.