It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for
text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and thus lack …

We study the problem of single-image zero-shot 3D shape reconstruction. Recent works
learn zero-shot shape reconstruction through generative modeling of 3D assets but these …

Recent advances in neural reconstruction enable high-quality 3D object reconstruction from
casually captured image collections. Current techniques mostly analyze their progress on …

High-quality 3D ground-truth shapes are critical for 3D object reconstruction evaluation.
However, it is difficult to create a replica of an object in reality, and even 3D reconstructions …

Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …

Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …

We present a new multi-sensor dataset for multi-view 3D surface reconstruction. It includes
registered RGB and depth data from sensors of different resolutions and modalities …

The overarching goal of this work is to enable the collection of language describing a wide
variety of objects viewed in virtual reality. We aim to create full 3D models from a small …

We study the influence of image interpolation algorithms on local feature detectors operating
on a scale pyramid, focusing on the Difference-of-Gaussian, as used in SIFT. We show that …

Numerous deep learning-based methods have been proposed for achieving high accuracy
in 3D object reconstruction. However, when examining the recent models, we observe that …