SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single
  Images
Authors: name: Zixuan Huang, name: Mark Boss, name: Aaryaman Vasishta, name: James M. Rehg, name: Varun Jampani

We study the problem of single-image 3D object reconstruction. Recent works
have diverged into two directions: regression-based modeling and generative
modeling. Regression methods efficiently infer visible surfaces, but struggle
with occluded regions. Generative methods handle uncertain regions better by
modeling distributions, but are computationally expensive and the generation is
often misaligned with visible surfaces. In this paper, we present SPAR3D, a
novel two-stage approach aiming to take the best of both directions. The first
stage of SPAR3D generates sparse 3D point clouds using a lightweight point
diffusion model, which has a fast sampling speed. The second stage uses both
the sampled point cloud and the input image to create highly detailed meshes.
Our two-stage design enables probabilistic modeling of the ill-posed
single-image 3D task while maintaining high computational efficiency and great
output fidelity. Using point clouds as an intermediate representation further
allows for interactive user edits. Evaluated on diverse datasets, SPAR3D
demonstrates superior performance over previous state-of-the-art methods, at an
inference speed of 0.7 seconds. Project page with code and model:
https://spar3d.github.io