Browse State-of-the-Art › 3D Shape Modeling
3D Shape Modeling
13 papers with code · 2 benchmarks · 7 datasets archive 2025-07-28
Image: Gkioxari 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 |
|---|---|---|---|---|---|
| Pix3D S1 (1 row) | Mesh R-CNN | Mesh R-CNN | code | — | Compare |
| Pix3D S2 (1 row) | Mesh R-CNN | Mesh R-CNN | code | — | 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
7 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
13 shown of 13 papers with code (28 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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6 Jun 2019 7 repositories listedWe propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object.
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4 Jan 2024 2 repositories listedIn this work, we propose "Temporal 3D ShapE Modeling for VCCRe-ID" (SEMI), a lightweight end-to-end framework that addresses these issues by learning human 3D shape representations.
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23 May 2025 1 repository listed Syntology ran 2 of 10 samples · 8 unverifiedGenerating high-resolution 3D shapes using volumetric representations such as Signed Distance Functions (SDFs) presents substantial computational and memory challenges.
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23 Dec 2024 1 repository listed Syntology ran 2 of 7 samples · 5 unverifiedHowever, the widely adopted uniform point sampling strategy in Shape VAE training often leads to a significant loss of geometric details, limiting the quality of shape reconstruction and downstream generation tasks.
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19 Jun 2023 1 repository listed3D shape modeling is labor-intensive, time-consuming, and requires years of expertise.
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18 Aug 2022 1 repository listedRecent progress in 4D implicit representation focuses on globally controlling the shape and motion with low dimensional latent vectors, which is prone to missing surface details and accumulating tracking error.
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27 May 2022 1 repository listedAll probabilistic experiments confirm that we are able to generate detailed and high quality shapes to yield the new state of the art in generative 3D shape modeling.
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31 Jan 2022 1 repository listed Syntology ran 0 of 6 samples · 6 unverifiedNeural implicit fields are quickly emerging as an attractive representation for learning based techniques.
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8 Dec 2021 1 repository listedThe paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to process parameters, which can be interpreted as…
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26 Jul 2020 1 repository listedGSNet utilizes a unique four-way feature extraction and fusion scheme and directly regresses 6DoF poses and shapes in a single forward pass.
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31 Jul 2019 1 repository listedTo our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.
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12 Apr 2018 1 repository listedWe study 3D shape modeling from a single image and make contributions to it in three aspects.
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24 Aug 2017 1 repository listedOur contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual…
Syntology lines on 3 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.
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