Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …

Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera
intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain yet …

We consider the problem of reconstructing a full 360deg photographic model of an object
from a single image of it. We do so by fitting a neural radiance field to the image, but find this …

Recent advances in large-scale pretraining have yielded visual foundation models with
strong capabilities. Not only can recent models generalize to arbitrary images for their …

In this work, we present a novel framework built to simplify 3D asset generation for amateur
users. To enable interactive generation, our method supports a variety of input modalities …

In this paper, we present a novel diffusion model called that generates multiview-consistent
images from a single-view image. Using pretrained large-scale 2D diffusion models, recent …

Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …

We propose pixelNeRF, a learning framework that predicts a continuous neural scene
representation conditioned on one or few input images. The existing approach for …

Reconstructing the 3D shape of an object from a single RGB image is a long-standing
problem in computer vision. In this paper, we propose a novel method for single-image 3D …