LIRA: Reasoning Reconstruction via Multimodal Large Language Models

Existing language instruction-guided online 3D reconstruction systems mainly rely on explicit instructions or queryable maps, showing inadequate capability to handle implicit and complex instructions. In this paper, we first introduce a reasoning reconstruction task. This task inputs an implicit instruction involving complex reasoning and an RGB-D sequence, and outputs incremental 3D reconstruction of instances that conform to the instruction. To handle this task, we propose LIRA: Language Instructed Reconstruction Assistant. It leverages a multimodal large language model to actively reason about the implicit instruction and obtain instruction-relevant 2D candidate instances and their attributes. Then, candidate instances are back-projected into the incrementally reconstructed 3D geometric map, followed by instance fusion and target instance inference. In LIRA, to achieve higher instance fusion quality, we propose TIFF, a Text-enhanced Instance Fusion module operating within Fragment bounding volume, which is learning-based and fuses multiple keyframes simultaneously. Since the evaluation system for this task is not well established, we propose a benchmark ReasonRecon comprising the largest collection of scene-instruction data samples involving implicit reasoning. Experiments demonstrate that LIRA outperforms existing methods in the reasoning reconstruction task and is capable of running in real time. Code and benchmark will be publicly available.