mlsanityData to 7 Oct 2026

Towards Generalising Neural Implicit Representations

arXiv 2101.12690 ↗First posted 29 Jan 2021cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 45 references that are in the map. First posted on arXiv 29 Jan 2021.

Builds on

32 of 45 references.

References · 32

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  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
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20 more
  1. 2018DeepVoxels: Learning Persistent 3D Feature Embeddingscited by 401
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  3. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  4. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  5. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 332
  6. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
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  13. 20193D Instance Segmentation via Multi-Task Metric Learningcited by 79
  14. 2018VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentationcited by 64
  15. 2019JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fieldscited by 62
  16. 2020Curriculum DeepSDFcited by 62
  17. 2019Deep Level Sets: Implicit Surface Representations for 3D Shape Inferencecited by 52
  18. 2019Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentationcited by 29
  19. 2020Coupling Explicit and Implicit Surface Representations for Generative 3D Modelingcited by 19
  20. 2020Semantic Implicit Neural Scene Representations With Semi-Supervised Trainingcited by 9

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1.

2 papers.

  1. 2021Neural Fields in Visual Computing and Beyondcited by 539
  2. 2023Contextualising Implicit Representations for Semantic Taskscited by 0