# A Simple and Scalable Shape Representation for 3D Reconstruction

arXiv: https://arxiv.org/abs/2005.04623 · HTML: https://mlsanity.com/paper/2005.04623/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2020.

## Built on by (most cited first, 5 of 5)

- [Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image](https://mlsanity.com/paper/2111.03098/) (2021)
- [Learning Compositional Shape Priors for Few-Shot 3D Reconstruction](https://mlsanity.com/paper/2106.06440/) (2021)
- [ShapeICP: Iterative Category-level Object Pose and Shape Estimation from Depth](https://mlsanity.com/paper/2408.13147/) (2024)
- [MGP-KAD: Multimodal Geometric Priors and Kolmogorov-Arnold Decoder for Single-View 3D Reconstruction in Complex Scenes](https://mlsanity.com/paper/2602.06158/) (2026)
- [3DMPE: 3D Multi-Perspective Embedding](https://mlsanity.com/paper/2607.04898/) (2026)

## Abstract (arXiv)

Deep learning applied to the reconstruction of 3D shapes has seen growing interest. A popular approach to 3D reconstruction and generation in recent years has been the CNN encoder-decoder model usually applied in voxel space. However, this often scales very poorly with the resolution limiting the effectiveness of these models. Several sophisticated alternatives for decoding to 3D shapes have been proposed typically relying on complex deep learning architectures for the decoder model. In this work, we show that this additional complexity is not necessary, and that we can actually obtain high quality 3D reconstruction using a linear decoder, obtained from principal component analysis on the signed distance function (SDF) of the surface. This approach allows easily scaling to larger resolutions. We show in multiple experiments that our approach is competitive with state-of-the-art methods. It also allows the decoder to be fine-tuned on the target task using a loss designed specifically for SDF transforms, obtaining further gains.