  Reconstructing textured meshes from colored point clouds is an important but
challenging task in 3D graphics and vision. Most existing methods predict
colors as implicit functions in 3D or UV space, suffering from blurry textures
or the lack of generalization capability. Addressing this, we propose
PointDreamer, a novel framework for textured mesh reconstruction from colored
point cloud. It produces meshes with enhanced fidelity and clarity by 2D image
inpainting, taking advantage of the mature techniques and massive data of 2D
vision. Specifically, we first project the input point cloud into 2D space to
generate sparse multi-view images, and then inpaint empty pixels utilizing a
pre-trained 2D diffusion model. Next, we design a novel Non-Border-First
strategy to unproject the colors of the inpainted dense images back to 3D
space, thus obtaining the final textured mesh. In this way, our PointDreamer
works in a zero-shot manner, requiring no extra training. Extensive qualitative
and quantitative experiments on various synthetic and real-scanned datasets
show the SoTA performance of PointDreamer, by significantly outperforming
baseline methods with 30\% improvement in LPIPS score (from 0.118 to 0.068).
Code at: https://github.com/YuQiao0303/PointDreamer.
