  This paper presents a neural architecture MVDiffusion++ for 3D object
reconstruction that synthesizes dense and high-resolution views of an object
given one or a few images without camera poses. MVDiffusion++ achieves superior
flexibility and scalability with two surprisingly simple ideas: 1) A
``pose-free architecture'' where standard self-attention among 2D latent
features learns 3D consistency across an arbitrary number of conditional and
generation views without explicitly using camera pose information; and 2) A
``view dropout strategy'' that discards a substantial number of output views
during training, which reduces the training-time memory footprint and enables
dense and high-resolution view synthesis at test time. We use the Objaverse for
training and the Google Scanned Objects for evaluation with standard novel view
synthesis and 3D reconstruction metrics, where MVDiffusion++ significantly
outperforms the current state of the arts. We also demonstrate a text-to-3D
application example by combining MVDiffusion++ with a text-to-image generative
model. The project page is at https://mvdiffusion-plusplus.github.io.
