EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation

• URL: http://arxiv.org/abs/2110.06679v1
• Date: Wed, 13 Oct 2021 12:38:01 GMT
• Title: EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation
• Authors: Shidi Li, Miaomiao Liu, Christian Walder
• Abstract summary: This paper tackles the problem of parts-aware point cloud generation. A simple modification of the Variational Auto-Encoder yields a joint model of the point cloud itself. In addition to the flexibility afforded by our disentangled representation, the inductive bias introduced by our joint modelling approach yields the state-of-the-art experimental results on the ShapeNet dataset.
• Score: 19.817166425038753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper tackles the problem of parts-aware point cloud generation. Unlike existing works which require the point cloud to be segmented into parts a priori, our parts-aware editing and generation is performed in an unsupervised manner. We achieve this with a simple modification of the Variational Auto-Encoder which yields a joint model of the point cloud itself along with a schematic representation of it as a combination of shape primitives. In particular, we introduce a latent representation of the point cloud which can be decomposed into a disentangled representation for each part of the shape. These parts are in turn disentangled into both a shape primitive and a point cloud representation, along with a standardising transformation to a canonical coordinate system. The dependencies between our standardising transformations preserve the spatial dependencies between the parts in a manner which allows meaningful parts-aware point cloud generation and shape editing. In addition to the flexibility afforded by our disentangled representation, the inductive bias introduced by our joint modelling approach yields the state-of-the-art experimental results on the ShapeNet dataset.

Related papers

• DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion [68.39543754708124]
  We introduce DiffFacto, a novel probabilistic generative model that learns the distribution of shapes with part-level control. Experiments show that our method is able to generate novel shapes with multiple axes of control. It achieves state-of-the-art part-level generation quality and generates plausible and coherent shapes.
  arXiv  Detail & Related papers  (2023-05-03T06:38:35Z)
• Autoregressive 3D Shape Generation via Canonical Mapping [92.91282602339398]
  transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation. In this paper, we aim to further exploit the power of transformers and employ them for the task of 3D point cloud generation. Our model can be easily extended to multi-modal shape completion as an application for conditional shape generation.
  arXiv  Detail & Related papers  (2022-04-05T03:12:29Z)
• PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [81.71904691925428]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We also design a new model, called PoinTr, that adopts a transformer encoder-decoder architecture for point cloud completion. Our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones.
  arXiv  Detail & Related papers  (2021-08-19T17:58:56Z)
• 3D Object Classification on Partial Point Clouds: A Practical Perspective [91.81377258830703]
  A point cloud is a popular shape representation adopted in 3D object classification. This paper introduces a practical setting to classify partial point clouds of object instances under any poses. A novel algorithm in an alignment-classification manner is proposed in this paper.
  arXiv  Detail & Related papers  (2020-12-18T04:00:56Z)
• Representing Point Clouds with Generative Conditional Invertible Flow Networks [15.280751949071016]
  We propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. We show that our method leverages generative invertible flow networks to learn embeddings as well as to generate point clouds. Our model offers competitive or superior quantitative results on benchmark datasets.
  arXiv  Detail & Related papers  (2020-10-07T18:30:47Z)
• LPMNet: Latent Part Modification and Generation for 3D Point Clouds [3.04585143845864]
  We propose a single end-to-end Autoencoder model that can handle generation and modification of both semantic parts, and global shapes. The proposed method supports part exchange between 3D point cloud models and composition by different parts to form new models by directly editing latent representations.
  arXiv  Detail & Related papers  (2020-08-08T17:24:37Z)
• ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds [78.25501874120489]
  We develop shape-aware adversarial 3D point cloud attacks by leveraging the learned latent space of a point cloud auto-encoder. Different from prior works, the resulting adversarial 3D point clouds reflect the shape variations in the 3D point cloud space while still being close to the original one.
  arXiv  Detail & Related papers  (2020-05-24T00:03:27Z)
• Shape-Oriented Convolution Neural Network for Point Cloud Analysis [59.405388577930616]
  Point cloud is a principal data structure adopted for 3D geometric information encoding. Shape-oriented message passing scheme dubbed ShapeConv is proposed to focus on the representation learning of the underlying shape formed by each local neighboring point.
  arXiv  Detail & Related papers  (2020-04-20T16:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.