eprintid: 10155879
rev_number: 10
eprint_status: archive
userid: 699
dir: disk0/10/15/58/79
datestamp: 2022-09-21 11:59:32
lastmod: 2022-09-21 11:59:33
status_changed: 2022-09-21 11:59:32
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Wang, H
creators_name: Liu, Q
creators_name: Yue, X
creators_name: Lasenby, J
creators_name: Kusner, MJ
title: Unsupervised Point Cloud Pre-training via Occlusion Completion
ispublished: pub
divisions: C05
divisions: F48
divisions: B04
divisions: UCL
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
abstract: We describe a simple pre-training approach for point clouds. It works in three steps: 1. Mask all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the occluded points; 3. Use the encoder weights as initialisation for downstream point cloud tasks. We find that even when we pre-train on a single dataset (ModelNet40), this method improves accuracy across different datasets and encoders, on a wide range of downstream tasks. Specifically, we show that our method outperforms previous pre-training methods in object classification, and both part-based and semantic segmentation tasks. We study the pre-trained features and find that they lead to wide downstream minima, have high transformation invariance, and have activations that are highly correlated with part labels. Code and data are available at: https://github.com/hansen7/OcCo.
date: 2021
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: https://doi.org/10.1109/ICCV48922.2021.00964
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1821281
doi: 10.1109/ICCV48922.2021.00964
isbn_13: 978-1-6654-2812-5
lyricists_name: Kusner, Matthew
lyricists_id: MKKUS92
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
pres_type: paper
publication: Proceedings of the IEEE International Conference on Computer Vision
pagerange: 9762-9772
event_title: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
event_location: Montreal, QC, Canada
event_dates: 11th-17th October 2021
book_title: Proceedings of the IEEE International Conference on Computer Vision
citation:        Wang, H;    Liu, Q;    Yue, X;    Lasenby, J;    Kusner, MJ;      (2021)    Unsupervised Point Cloud Pre-training via Occlusion Completion.                     In:  Proceedings of the IEEE International Conference on Computer Vision.  (pp. pp. 9762-9772).  Institute of Electrical and Electronics Engineers (IEEE)       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10155879/1/Wang_Unsupervised_Point_Cloud_Pre-Training_via_Occlusion_Completion_ICCV_2021_paper.pdf