SegPoint: Segment Any Point Cloud via Large Language Model

Abstract

Despite significant progress in 3D point cloud segmentation, existing methods primarily address specific tasks and depend on explicit instructions to identify targets, lacking the capability to infer and understand implicit user intentions in a unified framework. In this work, we propose a model, called , that leverages the reasoning capabilities of a multi-modal Large Language Model (LLM) to produce point-wise segmentation masks across a diverse range of tasks: 1) 3D instruction segmentation, 2) 3D referring segmentation, 3) 3D semantic segmentation, and 4) 3D open-vocabulary semantic segmentation. To advance 3D instruction research, we introduce a new benchmark, , designed to evaluate segmentation performance from complex and implicit instructional texts, featuring point cloud-instruction pairs. Our experimental results demonstrate that achieves competitive performance on established benchmarks such as ScanRefer for referring segmentation and ScanNet for semantic segmentation, while delivering outstanding outcomes on the dataset. To our knowledge, is the first model to address these varied segmentation tasks within a single framework, achieving satisfactory performance.