Browse State-of-the-Art › 3D Object Super-Resolution
3D Object Super-Resolution
2 papers with code · 0 benchmarks · 1 dataset archive 2025-07-28
3D object super-resolution is the task of up-sampling 3D objects.
( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
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
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
1 subtask in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
2 shown of 2 papers with code (2 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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27 Feb 2018 3 repositories listedWe consider the problem of scaling deep generative shape models to high-resolution.
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2 Apr 2018 1 repository listed Syntology ran 0 of 9 samples · 9 unverifiedThis paper proposes a 3D shape descriptor network, which is a deep convolutional energy-based model, for modeling volumetric shape patterns.
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.
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