2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation-Reference-Cited by-同舟云学术

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2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation

Published:2024-01-03 Issue: Volume: Page:7321-7330
ISSN:
Container-title:2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Unal Ozan¹,Dai Dengxin²,Hoyer Lukas¹,Can Yigit Baran¹,Van Gool Luc¹

Affiliation:

1. ETH Zurich

2. Huawei Technologies

Funder

Toyota Motor Europe

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10483279/10483282/10484032.pdf?arnumber=10484032

Reference44 articles.

1. TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

2. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

3. Mixmatch: A holistic approach to semi-supervised learning;Berthelot,2019

4. LIDAR–camera fusion for road detection using fully convolutional neural networks

5. Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning

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