Automatic Pseudo-LiDAR Annotation: Generation of Training Data for 3D Object Detection Networks-Reference-Cited by-同舟云学术

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Automatic Pseudo-LiDAR Annotation: Generation of Training Data for 3D Object Detection Networks

Published:2024 Issue: Volume:12 Page:14227-14237
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Oh Changsuk¹^ORCID,Jang Youngseok²^ORCID,Shim Dongseok³,Kim Changhyeon⁴,Kim Junha²,Kim H. Jin²^ORCID

Affiliation:

1. Department of Aerospace Engineering, Seoul National University, Gwanak-gu, South Korea

2. Department of Mechanical and Aerospace Engineering, Artificial Intelligence Institute, Seoul National University, Gwanak-gu, South Korea

3. Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-gu, South Korea

4. Samsung Research, Seocho-gu, South Korea

Funder

AI Institute at Seoul National University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10401931.pdf?arnumber=10401931

Reference51 articles.

1. PointPillars: Fast Encoders for Object Detection From Point Clouds

2. Frustum PointNets for 3D Object Detection from RGB-D Data

3. Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

4. SECOND: Sparsely Embedded Convolutional Detection

5. PA3DNet: 3-D Vehicle Detection With Pseudo Shape Segmentation and Adaptive Camera-LiDAR Fusion

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