Methods › Computer Vision › Convolutions › Submanifold Convolution

Submanifold Convolution

7 papers tagged archive 2025-07-28

Introduced by Benjamin Graham et al. in 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Submanifold Convolution (SC) is a spatially sparse convolution operation used for tasks with sparse data like semantic segmentation of 3D point clouds. An SC convolution computes the set of active sites in the same way as a regular convolution: it looks for the presence of any active sites in its receptive field of size fᵈ. If the input has size l then the output will have size (l − f + s)/s. Unlike a regular convolution, an SC convolution discards the ground state for non-active sites by assuming that the input from those sites is zero. For more details see the paper, or the official code here.

PaperSourceSee Code · facebookresearch/SparseConvNet

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 21 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Semantic Segmentation4
3D Instance Segmentation2
3D Object Detection2
3D Semantic Segmentation2
Clustering2
Instance Segmentation2
Object Detection2
Segmentation2
object-detection2
4D Spatio Temporal Semantic Segmentation1
Depth Estimation1
GPU1
LIDAR Semantic Segmentation1
Mamba1
Multi-Task Learning1
Panoptic Segmentation1
Representation Learning1
Robust 3D Semantic Segmentation1
Scene Understanding1
Self-Supervised Learning1

Usage over time archive 2025-07-28

Papers per year tagged with Submanifold Convolution: 2017 to 2025, peak 2 2 0 2017: 1 paper 2017 2018: 0 papers 2018 2019: 1 paper 2019 2020: 2 papers 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Convolutions

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