We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks
including unconditional shape generation, point cloud completion, and cross-modality …

This paper introduces a novel framework called DT-Net for 3D mesh reconstruction and
generation via Disentangled Topology. Beyond previous works, we learn a topology-aware …

Deep generative models have shown success in generating 3D shapes with different
representations. In this work, we propose Neural Volumetric Mesh Generator (NVMG) which …

We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks
including unconditional shape generation, point cloud completion, and cross-modality …

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for
the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current …

Humans perceive and construct the world as an arrangement of simple parametric models.
In particular, we can often describe man-made environments using volumetric primitives …

Deep learning has been successfully used for tasks in the 2D image domain. Research on
3D computer vision and deep geometry learning has also attracted attention. Considerable …

With the recent advances in hardware and rendering techniques, 3D models have emerged
everywhere in our life. Yet creating 3D shapes is arduous and requires significant …

Deep learning for 3D data has become a popular research theme in many fields. However,
most of the research on 3D data is based on voxels, 2D images, and point clouds. At actual …

Traditionally, engineering designs are created manually by experts. This process can be
time-consuming and requires significant computing resources. Designs are iteratively …