  We introduce RGB2Point, an unposed single-view RGB image to a 3D point cloud
generation based on Transformer. RGB2Point takes an input image of an object
and generates a dense 3D point cloud. Contrary to prior works based on CNN
layers and diffusion denoising approaches, we use pre-trained Transformer
layers that are fast and generate high-quality point clouds with consistent
quality over available categories. Our generated point clouds demonstrate high
quality on a real-world dataset, as evidenced by improved Chamfer distance
(51.15%) and Earth Mover's distance (45.96%) metrics compared to the current
state-of-the-art. Additionally, our approach shows a better quality on a
synthetic dataset, achieving better Chamfer distance (39.26%), Earth Mover's
distance (26.95%), and F-score (47.16%). Moreover, our method produces 63.1%
more consistent high-quality results across various object categories compared
to prior works. Furthermore, RGB2Point is computationally efficient, requiring
only 2.3GB of VRAM to reconstruct a 3D point cloud from a single RGB image, and
our implementation generates the results 15,133x faster than a SOTA
diffusion-based model.
