  Although great progress has been made in 3D visual grounding, current models
still rely on explicit textual descriptions for grounding and lack the ability
to reason human intentions from implicit instructions. We propose a new task
called 3D reasoning grounding and introduce a new benchmark ScanReason which
provides over 10K question-answer-location pairs from five reasoning types that
require the synerization of reasoning and grounding. We further design our
approach, ReGround3D, composed of the visual-centric reasoning module empowered
by Multi-modal Large Language Model (MLLM) and the 3D grounding module to
obtain accurate object locations by looking back to the enhanced geometry and
fine-grained details from the 3D scenes. A chain-of-grounding mechanism is
proposed to further boost the performance with interleaved reasoning and
grounding steps during inference. Extensive experiments on the proposed
benchmark validate the effectiveness of our proposed approach.
