title: ZeroShape: Regression-based Zero-shot Shape Reconstruction
summary: We study the problem of single-image zero-shot 3D shape reconstruction.
  Recent works learn zero-shot shape reconstruction through generative modeling
  of 3D assets, but these models are computationally expensive at train and
  inference time. In contrast, the traditional approach to this problem is
  regression-based, where deterministic models are trained to directly regress
  the object shape. Such regression methods possess much higher computational
  efficiency than generative methods. This raises a natural question: is
  generative modeling necessary for high performance, or conversely, are
  regression-based approaches still competitive? To answer this, we design a
  strong regression-based model, called ZeroShape, based on the converging
  findings in this field and a novel insight. We also curate a large real-world
  evaluation benchmark, with objects from three different real-world 3D datasets.
  This evaluation benchmark is more diverse and an order of magnitude larger than
  what prior works use to quantitatively evaluate their models, aiming at
  reducing the evaluation variance in our field. We show that ZeroShape not only
  achieves superior performance over state-of-the-art methods, but also
  demonstrates significantly higher computational and data efficiency.
paper_authors._id: 658a60e816a6a0082058345e
paper_authors.name: Zixuan Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-05T10:01:20.608Z
paper_authors._id: 629fe0dd198a9a5a755f3ce0
paper_authors.avatarUrl: /avatars/e643d43f66c10729f155edca96aef1f8.svg
paper_authors.fullname: Zixuan Huang
paper_authors.type: user
paper_authors.user: zxhuang1698
paper_authors._id: 658a60e816a6a0082058345f
paper_authors.name: Stefan Stojanov
paper_authors._id: 658a60e816a6a00820583460
paper_authors.name: Anh Thai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-25T07:36:00.706Z
paper_authors._id: 653f30084a52f10eaf8381b8
paper_authors.avatarUrl: /avatars/ffd938c2c828c4c8cc7204eb5424b3bf.svg
paper_authors.fullname: Anh Thai
paper_authors.type: user
paper_authors.user: ngailapdi
paper_authors._id: 658a60e816a6a00820583461
paper_authors.name: Varun Jampani
paper_authors._id: 658a60e816a6a00820583462
paper_authors.name: James M. Rehg
paper_title: ZeroShape: Regression-based Zero-shot Shape Reconstruction
paper_summary: We study the problem of single-image zero-shot 3D shape reconstruction.
  Recent works learn zero-shot shape reconstruction through generative modeling
  of 3D assets, but these models are computationally expensive at train and
  inference time. In contrast, the traditional approach to this problem is
  regression-based, where deterministic models are trained to directly regress
  the object shape. Such regression methods possess much higher computational
  efficiency than generative methods. This raises a natural question: is
  generative modeling necessary for high performance, or conversely, are
  regression-based approaches still competitive? To answer this, we design a
  strong regression-based model, called ZeroShape, based on the converging
  findings in this field and a novel insight. We also curate a large real-world
  evaluation benchmark, with objects from three different real-world 3D datasets.
  This evaluation benchmark is more diverse and an order of magnitude larger than
  what prior works use to quantitatively evaluate their models, aiming at
  reducing the evaluation variance in our field. We show that ZeroShape not only
  achieves superior performance over state-of-the-art methods, but also
  demonstrates significantly higher computational and data efficiency.
paper_ai_summary: A regression-based model named ZeroShape achieves superior performance and higher computational efficiency in single-image zero-shot 3D shape reconstruction compared to generative models.
paper_ai_keywords: zero-shot 3D shape reconstruction generative modeling regression-based models ZeroShape 3D datasets evaluation benchmark