Browse State-of-the-Art › 3D Object Retrieval
3D Object Retrieval
11 papers with code · 2 benchmarks · 4 datasets archive 2025-07-28
Source: He et al
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
2 leaderboard tables shown for this task, 2 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.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| ModelNet40 (1 row) | MVTN | MVTN: Multi-View Transformation Network for 3D Shape Recognition | code | Syntology ran 3 of 3 samples · 0 unverified | Compare |
| ShapeNetCore 55 (1 row) | MVTN | MVTN: Multi-View Transformation Network for 3D Shape Recognition | code | Syntology ran 3 of 3 samples · 0 unverified | Compare |
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.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
11 shown of 11 papers with code (29 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.
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19 Nov 2018 4 repositories listedDeep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
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12 Sep 2023 3 repositories listedWe present an automated and efficient approach for retrieving high-quality CAD models of objects and their poses in a scene captured by a moving RGB-D camera.
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22 Dec 2022 2 repositories listedWe present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans.
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26 Nov 2020 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
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5 May 2025 1 repository listedLearning discriminative 3D representations that generalize well to unknown testing categories is an emerging requirement for many real-world 3D applications.
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2 May 2024 1 repository listedThe scheme ensures that the denoising processes are influenced by a holistic understanding of the scene graph, facilitating the generation of globally coherent scenes.
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3 Dec 2021 1 repository listedWe present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image.
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7 Jul 2021 1 repository listedA complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree.
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2 Oct 2020 1 repository listedFine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed…
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7 Aug 2020 1 repository listedA binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented.
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16 Mar 2018 1 repository listedMost existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with…
Syntology lines on 1 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections