# SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation

Source: [SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6f2a1e209131cea4430a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:14:37.532Z

Authors: Olivier Therrien, Marihan Amein, Zhuoran Xiong, Warren J. Gross, Brett H. Meyer

Published: 2023-04-21T18:29:34.000Z

## Abstract

We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks. It uses RandLA-Net, an off-the-shelf point-based network, as a super-network to enable weight sharing and reduce search time by 99.67% for single-stage searches. SSS3D has a complex search space composed of sampling and architectural parameters that can form 2.88 \* 10^17 possible networks. To further reduce search time, SSS3D splits the complete search space and introduces a two-stage search that finds optimal subnetworks in 54% of the time required by single-stage searches.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.11207>)

[PDF](<https://arxiv.org/pdf/2304.11207.pdf>)