  We introduce a novel 3D generative method, Generative 3D Reconstruction
(G3DR) in ImageNet, capable of generating diverse and high-quality 3D objects
from single images, addressing the limitations of existing methods. At the
heart of our framework is a novel depth regularization technique that enables
the generation of scenes with high-geometric fidelity. G3DR also leverages a
pretrained language-vision model, such as CLIP, to enable reconstruction in
novel views and improve the visual realism of generations. Additionally, G3DR
designs a simple but effective sampling procedure to further improve the
quality of generations. G3DR offers diverse and efficient 3D asset generation
based on class or text conditioning. Despite its simplicity, G3DR is able to
beat state-of-theart methods, improving over them by up to 22% in perceptual
metrics and 90% in geometry scores, while needing only half of the training
time. Code is available at https://github.com/preddy5/G3DR
