Zero-Shot Transfer 3D Point Cloud Classification

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 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

2 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (11 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.

• Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
  5 Feb 2023 5 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
• ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
  27 Feb 2024 3 repositories listed Syntology ran 9 of 17 samples · 8 unverified
  This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
• Uni3D: Exploring Unified 3D Representation at Scale
  10 Oct 2023 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.
• 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.
• OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images
  25 Apr 2024 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  In this paper, we present OpenDlign, a novel open-world 3D model using depth-aligned images generated from a diffusion model for robust multimodal alignment.
• Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training
  3 Nov 2023 1 repository listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
  Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i.
• ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
  20 Aug 2023 1 repository listed
  A well-trained lens with a ViT backbone has the potential to serve as one of these foundation models, supervising the learning of subsequent modalities.
• OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
  18 May 2023 1 repository listed Syntology ran 0 of 7 samples · 7 unverified
  Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point…
• ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
  10 Dec 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Then, ULIP learns a 3D representation space aligned with the common image-text space, using a small number of automatically synthesized triplets.
• CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
  3 Oct 2022 1 repository listed
  To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification.

Syntology lines on 8 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.