  We present a novel method for reconstructing 3D objects from a single RGB
image. Our method leverages the latest image generation models to infer the
hidden 3D structure while remaining faithful to the input image. While existing
methods obtain impressive results in generating 3D models from text prompts,
they do not provide an easy approach for conditioning on input RGB data.
Na\"ive extensions of these methods often lead to improper alignment in
appearance between the input image and the 3D reconstructions. We address these
challenges by introducing Image Constrained Radiance Fields (ConRad), a novel
variant of neural radiance fields. ConRad is an efficient 3D representation
that explicitly captures the appearance of an input image in one viewpoint. We
propose a training algorithm that leverages the single RGB image in conjunction
with pretrained Diffusion Models to optimize the parameters of a ConRad
representation. Extensive experiments show that ConRad representations can
simplify preservation of image details while producing a realistic 3D
reconstruction. Compared to existing state-of-the-art baselines, we show that
our 3D reconstructions remain more faithful to the input and produce more
consistent 3D models while demonstrating significantly improved quantitative
performance on a ShapeNet object benchmark.
