This paper presents a neural architecture MVDiffHD for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses. MVDiffHD 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) Aview 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 MVDiffHD significantly outperforms the current state of the arts. We also demonstrate a text-to-3D application example by combining MVDiffHD with a text-to-image generative model.