Methods › Computer Vision › 3D Object Detection Models › 3DSSD

3DSSD

1 paper tagged archive 2025-07-28

Introduced by Zetong Yang et al. in 3DSSD: Point-based 3D Single Stage Object Detector

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

3DSSD is a point-based 3D single stage object detection detector. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. The authors propose a fusion sampling strategy in the downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet the needs of accuracy and speed.

PaperSource

Papers archive 2025-07-28

1 shown of 1, 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

1 task the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object1

Usage over time archive 2025-07-28

Papers per year tagged with 3DSSD: 2020 to 2020, peak 1 1 0 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (1 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

3D Object Detection Models

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