# SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation

Source: [SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation](<https://www.sota2.com/research/paper/68fa69b41e209131cea23954>)

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

Data updated: 2025-10-25T23:06:39.153Z

Authors: Hani Itani, Silvio Giancola, Ali Thabet, Bernard Ghanem

Published: 2020-12-29T20:16:37.000Z

## Abstract

In this work, we focus on designing a point local aggregation function that yields parameter efficient networks for 3D point cloud semantic segmentation. We explore the idea of using learnable neighbor-to-grid soft assignment in grid-based aggregation functions. Previous methods in literature operate on a predefined geometric grid such as local volume partitions or irregular kernel points. A more general alternative is to allow the network to learn an assignment function that best suits the end task. Since it is learnable, this mapping is allowed to be different per layer instead of being applied uniformly throughout the depth of the network. By endowing the network with the flexibility to learn its own neighbor-to-grid assignment, we arrive at parameter efficient models that achieve state-of-the-art (SOTA) performance on S3DIS with at least 10$\\times$ less parameters than the current reigning method. We also demonstrate competitive performance on ScanNet and PartNet compared with much larger SOTA models.

## 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/2012.14929>)

[PDF](<https://arxiv.org/pdf/2012.14929.pdf>)