# Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding

Source: [Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding](<https://www.sota2.com/research/paper/68fa6e571e209131cea3f300>)

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

Data updated: 2025-10-26T00:55:03.570Z

Authors: Chengzhi Wu, Julius Pfrommer, J\\"urgen Beyerer, Kangning Li, Boris Neubert

Published: 2023-01-11T18:14:47.000Z

## Abstract

We present an improved approach for 3D object detection in point cloud data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed method considers the point neighborhood when computing point features. The newly introduced local neighborhood embedding operation mimics the convolutional operations in 2D neural networks. Thus features of each point are not only computed with the features of its own or of the whole point cloud but also computed especially with respect to the features of its neighbors. Experiments show that our proposed method achieves better performance than the F-Pointnet baseline on 3D object detection tasks.

## 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/2301.04613>)

[PDF](<https://arxiv.org/pdf/2301.04613.pdf>)