The reconstruction of 3D object from a single image is an important task in the field of
computer vision. In recent years, 3D reconstruction of single image using deep learning …

Although the recent rapid evolution of 3D generative neural networks greatly improves 3D
shape generation, it is still not convenient for ordinary users to create 3D shapes and control …

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades
by the computer vision, computer graphics, and machine learning communities. Since 2015 …

Convolutional networks for single-view object reconstruction have shown impressive
performance and have become a popular subject of research. All existing techniques are …

We present an approach to semantic scene analysis using deep convolutional networks.
Our approach is based on tangent convolutions-a new construction for convolutional …

Text-to-image diffusion models are gradually introduced into computer graphics, recently
enabling the development of Text-to-3D pipelines in an open domain. However, for …

We present multiresolution tree-structured networks to process point clouds for 3D shape
understanding and generation tasks. Our network represents a 3D shape as set of locality …

We propose pix2pix3D, a 3D-aware conditional generative model for controllable
photorealistic image synthesis. Given a 2D label map, such as a segmentation or edge map …

We present an Adaptive Octree-based Convolutional Neural Network (Adaptive O-CNN) for
efficient 3D shape encoding and decoding. Different from volumetric-based or octree-based …

We introduce PQ-NET, a deep neural network which represents and generates 3D shapes
via sequential part assembly. The input to our network is a 3D shape segmented into parts …