Single-View 3D Reconstruction via SO(2)-Equivariant Gaussian Sculpting Networks
<|reference_start|>Single-View 3D Reconstruction via SO(2)-Equivariant Gaussian Sculpting Networks: This paper introduces SO(2)-Equivariant Gaussian Sculpting Networks (GSNs) as an approach for SO(2)-Equivariant 3D object reconstruction from single-view image observations.   GSNs take a single observation as input to generate a Gaussian splat representation describing the observed object's geometry and texture. By using a shared feature extractor before decoding Gaussian colors, covariances, positions, and opacities, GSNs achieve extremely high throughput (>150FPS). Experiments demonstrate that GSNs can be trained efficiently using a multi-view rendering loss and are competitive, in quality, with expensive diffusion-based reconstruction algorithms. The GSN model is validated on multiple benchmark experiments. Moreover, we demonstrate the potential for GSNs to be used within a robotic manipulation pipeline for object-centric grasping.<|reference_end|>
@article{xu2024single-view,
    title={Single-View 3D Reconstruction via SO(2)-Equivariant Gaussian Sculpting
  Networks},
    author={Ruihan Xu, Anthony Opipari, Joshua Mah, Stanley Lewis, Haoran Zhang,
  Hanzhe Guo, Odest Chadwicke Jenkins},
    journal={arXiv preprint arXiv:2409.07245},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07245},
    primaryClass={cs.CV cs.RO}
}