Datasets › 3D MM-Vet
3D MM-Vet
We established a 3D evaluation benchmark, 3D MM-Vet, to assess the 4-level capacity in embodied interaction scenarios, varying from basic perception to control statements generation.
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| 3D Question Answering (3D-QA) | 3D MM-Vet | ShapeLLM-13B Overall Accuracy 53.1 | ShapeLLM: Universal 3D Object Understanding for Embodied... | qizekun/ShapeLLM +2 | 5 | Compare |
Papers archive 2025-07-28
3 shown of 3 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 4. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| ShapeLLM: Universal 3D Object Understanding for Embodied Interaction | 3 | 2 | 27 Feb 2024 | ran 9 of 17 samples (8 unverified) |
| Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following | 5 | 1 | 1 Sep 2023 | ran 13 of 20 samples (7 unverified; 7 pointer-only for licence) |
| PointLLM: Empowering Large Language Models to Understand Point Clouds | 3 | 2 | 31 Aug 2023 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- 3D MM-Vet
1 variant name, as the archive lists them.
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