# LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds

arXiv: https://arxiv.org/abs/1904.10037 · HTML: https://mlsanity.com/paper/1904.10037/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [Point-set Distances for Learning Representations of 3D Point Clouds](https://mlsanity.com/paper/2102.04014/) (2021)
- [Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization](https://mlsanity.com/paper/2111.12878/) (2021)
- [Generalizing Neural Human Fitting to Unseen Poses With Articulated SE(3) Equivariance](https://mlsanity.com/paper/2304.10528/) (2023)
- [Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach](https://mlsanity.com/paper/2109.14982/) (2021)
- [Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds](https://mlsanity.com/paper/2107.07539/) (2021)
- [Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects](https://mlsanity.com/paper/2308.12590/) (2023)
- [Deep Virtual Markers for Articulated 3D Shapes](https://mlsanity.com/paper/2108.09000/) (2021)
- [A Near-Linear Time Algorithm for the Chamfer Distance](https://mlsanity.com/paper/2307.03043/) (2023)
- [Unsupervised Dense Deformation Embedding Network for Template-Free Shape Correspondence](https://mlsanity.com/paper/2108.11609/) (2021)
- [CD2: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance](https://mlsanity.com/paper/2206.00447/) (2022)
- [Isomorphic mesh generation from point clouds with multilayer perceptrons](https://mlsanity.com/paper/2210.14157/) (2022)
- [AnthroNet: Conditional Generation of Humans via Anthropometrics](https://mlsanity.com/paper/2309.03812/) (2023)
- [U-mesh: Human Correspondence Matching with Mesh Convolutional Networks](https://mlsanity.com/paper/2108.06695/) (2021)
- [3D Human Mesh Estimation from Single View RGBD](https://mlsanity.com/paper/2508.08178/) (2025)

## Abstract (arXiv)

We present LBS-AE; a self-supervised autoencoding algorithm for fitting articulated mesh models to point clouds. As input, we take a sequence of point clouds to be registered as well as an artist-rigged mesh, i.e. a template mesh equipped with a linear-blend skinning (LBS) deformation space parameterized by a skeleton hierarchy. As output, we learn an LBS-based autoencoder that produces registered meshes from the input point clouds. To bridge the gap between the artist-defined geometry and the captured point clouds, our autoencoder models pose-dependent deviations from the template geometry. During training, instead of using explicit correspondences, such as key points or pose supervision, our method leverages LBS deformations to bootstrap the learning process. To avoid poor local minima from erroneous point-to-point correspondences, we utilize a structured Chamfer distance based on part-segmentations, which are learned concurrently using self-supervision. We demonstrate qualitative results on real captured hands, and report quantitative evaluations on the FAUST benchmark for body registration. Our method achieves performance that is superior to other unsupervised approaches and comparable to methods using supervised examples.