Learning to generate 3D point clouds without 3D supervision is an important but challenging
problem. Current solutions leverage various differentiable renderers to project the generated …

We present a self-supervised human mesh recovery framework to infer human pose and
shape from monocular images in the absence of any paired supervision. Recent advances …

Differentiable renderers have been used successfully for unsupervised 3D structure learning
from 2D images because they can bridge the gap between 3D and 2D. To optimize 3D …

It is challenging to reconstruct 3D point clouds in unseen classes from single 2D images.
Instead of object-centered coordinate system, current methods generalized global priors …

Training deep neural networks to estimate the viewpoint of objects requires large labeled
training datasets. However, manually labeling viewpoints is notoriously hard, error-prone …

Reconstructing 3D models from 2D images is one of the fundamental problems in computer
vision. In this work, we propose a deep learning technique for 3D object reconstruction from …

Abstract Reasoning 3D shapes from 2D images is an essential yet challenging task,
especially when only single-view images are at our disposal. While an object can have a …

Airborne LiDAR observations are very effective for providing accurate 3D point clouds, and
archived data are becoming available to the public. In many cases, only geometric …

Abstract Structure learning for 3D shapes is vital for 3D computer vision. State-of-the-art
methods show promising results by representing shapes using implicit functions in 3D that …

Implicit 3D representations have shown great promise in deep learning-based 3D
reconstruction. With differentiable renderers, current methods are able to learn implicit …