UNeR3D: Versatile and Scalable 3D RGB Point Cloud Generation from 2D
  Images in Unsupervised Reconstruction
Hongbin Lin Juangui Xu Qingfeng Xu Zhengyu Hu Handing Xu Yunzhi Chen Yongjun Hu Zhenguo Nie
In the realm of 3D reconstruction from 2D images, a persisting challenge is
to achieve high-precision reconstructions devoid of 3D Ground Truth data
reliance. We present UNeR3D, a pioneering unsupervised methodology that sets a
new standard for generating detailed 3D reconstructions solely from 2D views.
Our model significantly cuts down the training costs tied to supervised
approaches and introduces RGB coloration to 3D point clouds, enriching the
visual experience. Employing an inverse distance weighting technique for color
rendering, UNeR3D ensures seamless color transitions, enhancing visual
fidelity. Our model's flexible architecture supports training with any number
of views, and uniquely, it is not constrained by the number of views used
during training when performing reconstructions. It can infer with an arbitrary
count of views during inference, offering unparalleled versatility.
Additionally, the model's continuous spatial input domain allows the generation
of point clouds at any desired resolution, empowering the creation of
high-resolution 3D RGB point clouds. We solidify the reconstruction process
with a novel multi-view geometric loss and color loss, demonstrating that our
model excels with single-view inputs and beyond, thus reshaping the paradigm of
unsupervised learning in 3D vision. Our contributions signal a substantial leap
forward in 3D vision, offering new horizons for content creation across diverse
applications. Code is available at https://github.com/HongbinLin3589/UNeR3D.