3D Shape Retrieval

Latest papers

8 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

GATE-3D: Geometry-Aware Test-time Adaptive Reranking for Open-Set 3D Shape Retrieval
Jul 21, 2026Hao Wu, Heyi Lin, Zilin Wang +33D Shape Retrieval Learning to Rank
Hong Kong University of Science and Technology Hong Kong, China · Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
Frequency-Scale Saliency for Spectral Descriptor Analysis in 3D Shape Retrieval
Jun 5, 2026Jianru Shen3D Shape Retrieval Spectral Methods
University of Montana, Missoula, MT 59812, USA
BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding
Jun 3, 2026Muhammad Usama, Didier Stricker, Mohammad Sadil Khan +13D Shape Retrieval Multimodal Pretraining
1DFKI, Germany · 2RPTU Kaiserslautern-Landau, Germany
Auditing Training-Free 3D Shape Retrieval with Diffused Geodesic Moments
May 27, 2026Zhicheng Du, Changyue Liu, Wenji Xi +53D Shape Retrieval
Tsinghua Shenzhen International Graduate School, Tsinghua University · Guangzhou International Economics College · School of Electrical and Electronic Engineering, The University of Sheffield
PASR: Pose-Aware 3D Shape Retrieval from Occluded Single Views
Apr 24, 2026Jiaxin Shi, Guofeng Zhang, Wufei Ma +33D Shape Retrieval 3D Representation Learning
1Shanghai Jiao Tong University · 2Johns Hopkins University · University of California, San Diego +1
CanoVerse: 3D Object Scalable Canonicalization and Dataset for Generation and Pose
Mar 7, 2026Li Jin, Yuchen Yang, Weikai Chen +113D Shape Retrieval 3D Representation Learning
SDU · LIGHTSPEED · UNC Chapel Hill +2
Optimizing Multi-Modal Models for Image-Based Shape Retrieval: The Role of Pre-Alignment and Hard Contrastive Learning
Mar 7, 2026Paul Julius Kühn, Cedric Spengler, Michael Weinmann +23D Shape Retrieval Cross-Modal Retrieval
Fraunhofer IGD, 64283 Darmstadt, Germany · Delft University of Technology, 2628 CD Delft, Netherlands
PartRAG: Retrieval-Augmented Part-Level 3D Generation and Editing
Feb 19, 2026Peize Li, Zeyu Zhang, Hao Tang3D Shape Retrieval Part-Level 3D Generation
King’s College London · Peking University

Jul 21, 2026J/K move · Enter open · S save

GATE-3D: Geometry-Aware Test-time Adaptive Reranking for Open-Set 3D Shape Retrieval

Hao Wu, Heyi Lin, Zilin Wang, Huizai Yao+2

Hong Kong University of Science and Technology Hong Kong, China · Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China

3D Shape Retrieval Learning to Rank Cross-Modal Retrieval

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Large pretrained vision models have substantially improved appearance-based 3D shape retrieval, but they still confuse shapes that look similar while differing in geometry. Although geometry-aware features can reduce these errors, naive fusion of geometry and appearance may hurt retrieval when the two modalities are already well aligned. We propose GATE-3D, a lightweight query-adaptive reranking method that incorporates geometry without retraining the retrieval backbone. For each query, GATE-3D predicts how much a geometry-aware score should adjust the appearance-based ranking using features that capture disagreement between the two modalities. This selective design lets geometry contribute where it helps and stay silent where it would hurt. Experiments on three open-set 3D retrieval benchmarks show that GATE-3D improves over appearance-only retrieval and is more robust than always-on fusion. On the primary benchmark, it improves mAP@10 by 2.00 points over appearance-only retrieval (p=0.041); it also improves leave-one-category-out generalization and reduces geometric false positives by 10.8%. GATE-3D achieves competitive zero-shot results against DAC-based baselines. We further find that simple linear routing is more effective than a small MLP in the low-data regime, suggesting that cross-modal disagreement features matter more than model capacity for adaptive routing.