3D Open-Vocabulary Instance Segmentation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (14 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
  21 Nov 2022 2 repositories listed Syntology ran 5 of 12 samples · 7 unverified
  In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
• PointCLIP: Point Cloud Understanding by CLIP
  4 Dec 2021 2 repositories listed
  On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D.
• Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation
  4 Jun 2024 1 repository listed Syntology ran 4 of 11 samples · 7 unverified · 11 pointer-only (licence)
  To this end, we propose a fast yet accurate open-vocabulary 3D instance segmentation approach, named Open-YOLO 3D, that effectively leverages only 2D object detection from multi-view RGB images for open-vocabulary 3D…
• Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance
  17 Dec 2023 1 repository listed Syntology ran 8 of 8 samples · 0 unverified
  We introduce Open3DIS, a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes.
• OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data
  6 Nov 2023 1 repository listed Syntology ran 9 of 9 samples · 0 unverified
  This work presents OVIR-3D, a straightforward yet effective method for open-vocabulary 3D object instance retrieval without using any 3D data for training.
• OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
  1 Sep 2023 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  In this work, we introduce OpenIns3D, a new 3D-input-only framework for 3D open-vocabulary scene understanding.
• OpenMask3D: Open-Vocabulary 3D Instance Segmentation
  23 Jun 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  In this work, we address this limitation, and propose OpenMask3D, which is a zero-shot approach for open-vocabulary 3D instance segmentation.
• PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
  29 Nov 2022 1 repository listed
  Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space.
• OpenScene: 3D Scene Understanding with Open Vocabularies
  28 Nov 2022 1 repository listed Syntology ran 4 of 9 samples · 5 unverified
  Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.