OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding

• URL: http://arxiv.org/abs/2601.09575v1
• Date: Wed, 14 Jan 2026 15:45:57 GMT
• Title: OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding
• Abstract summary: We propose OpenVoxel, a training-free algorithm for grouping and captioningvoxels for the open-vocabulary 3D scene understanding tasks.<n>Our method demonstrates superior performance compared to recent studies, particularly in complex referring expression segmentation (RES) tasks.
• Score: 51.20680201565003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose OpenVoxel, a training-free algorithm for grouping and captioning sparse voxels for the open-vocabulary 3D scene understanding tasks. Given the sparse voxel rasterization (SVR) model obtained from multi-view images of a 3D scene, our OpenVoxel is able to produce meaningful groups that describe different objects in the scene. Also, by leveraging powerful Vision Language Models (VLMs) and Multi-modal Large Language Models (MLLMs), our OpenVoxel successfully build an informative scene map by captioning each group, enabling further 3D scene understanding tasks such as open-vocabulary segmentation (OVS) or referring expression segmentation (RES). Unlike previous methods, our method is training-free and does not introduce embeddings from a CLIP/BERT text encoder. Instead, we directly proceed with text-to-text search using MLLMs. Through extensive experiments, our method demonstrates superior performance compared to recent studies, particularly in complex referring expression segmentation (RES) tasks. The code will be open.

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