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3D open-vocabulary scene understanding aims to recognize arbitrary novelcategories beyond the base label space. However, existing works not only failto fully utilize all the available modal information in the 3D domain but alsolack sufficient granularity in representing the features of each modality. Inthis paper, we propose a unified multimodal 3D open-vocabulary sceneunderstanding network, namely UniM-OV3D, which aligns point clouds with image,language and depth. To better integrate global and local features of the pointclouds, we design a hierarchical point cloud feature extraction module thatlearns comprehensive fine-grained feature representations. Further, tofacilitate the learning of coarse-to-fine point-semantic representations fromcaptions, we propose the utilization of hierarchical 3D caption pairs,capitalizing on geometric constraints across various viewpoints of 3D scenes.Extensive experimental results demonstrate the effectiveness and superiority ofour method in open-vocabulary semantic and instance segmentation, whichachieves state-of-the-art performance on both indoor and outdoor benchmarkssuch as ScanNet, ScanNet200, S3IDS and nuScenes. Code is available athttps://github.com/hithqd/UniM-OV3D.

UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation