Towards generalising neural implicit representations
Neural implicit representations have shown substantial improvements in efficiently storing 3D data, when compared to conventional formats. However, the focus of existing work has mainly been on storage and subsequent reconstruction. In this work, we show that training neural representations for reco...
| Main Authors: | , |
|---|---|
| Format: | Internet publication |
| Language: | English |
| Published: |
2021
|
| _version_ | 1811139637369896960 |
|---|---|
| author | Costain, TW Prisacariu, VA |
| author_facet | Costain, TW Prisacariu, VA |
| author_sort | Costain, TW |
| collection | OXFORD |
| description | Neural implicit representations have shown substantial improvements in efficiently storing 3D data, when compared to conventional formats. However, the focus of existing work has mainly been on storage and subsequent reconstruction. In this work, we show that training neural representations for reconstruction tasks alongside conventional tasks can produce more general encodings that admit equal quality reconstructions to single task training, whilst improving results on conventional tasks when compared to single task encodings. We reformulate the semantic segmentation task, creating a more representative task for implicit representation contexts, and through multi-task experiments on reconstruction, classification, and segmentation, show our approach learns feature rich encodings that admit equal performance for each task. |
| first_indexed | 2024-09-25T04:09:15Z |
| format | Internet publication |
| id | oxford-uuid:a3423889-14e1-439d-a56d-839a69339dbe |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:09:15Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | oxford-uuid:a3423889-14e1-439d-a56d-839a69339dbe2024-06-11T16:52:21ZTowards generalising neural implicit representationsInternet publicationhttp://purl.org/coar/resource_type/c_7ad9uuid:a3423889-14e1-439d-a56d-839a69339dbeEnglishSymplectic Elements2021Costain, TWPrisacariu, VANeural implicit representations have shown substantial improvements in efficiently storing 3D data, when compared to conventional formats. However, the focus of existing work has mainly been on storage and subsequent reconstruction. In this work, we show that training neural representations for reconstruction tasks alongside conventional tasks can produce more general encodings that admit equal quality reconstructions to single task training, whilst improving results on conventional tasks when compared to single task encodings. We reformulate the semantic segmentation task, creating a more representative task for implicit representation contexts, and through multi-task experiments on reconstruction, classification, and segmentation, show our approach learns feature rich encodings that admit equal performance for each task. |
| spellingShingle | Costain, TW Prisacariu, VA Towards generalising neural implicit representations |
| title | Towards generalising neural implicit representations |
| title_full | Towards generalising neural implicit representations |
| title_fullStr | Towards generalising neural implicit representations |
| title_full_unstemmed | Towards generalising neural implicit representations |
| title_short | Towards generalising neural implicit representations |
| title_sort | towards generalising neural implicit representations |
| work_keys_str_mv | AT costaintw towardsgeneralisingneuralimplicitrepresentations AT prisacariuva towardsgeneralisingneuralimplicitrepresentations |