We propose a data-driven scene flow estimation algorithm exploiting the observation that
many 3D scenes can be explained by a collection of agents moving as rigid bodies. At the …

We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a
part-aware manner. Once trained, the network can generate novel textured meshes from …

Recent advances in localized implicit functions have enabled neural implicit representation
to be scalable to large scenes. However, the regular subdivision of 3D space employed by …

Neural implicit function based on signed distance field (SDF) has achieved impressive
progress in reconstructing 3D models with high fidelity. However, such approaches can only …

Point clouds have garnered increasing research attention and found numerous practical
applications. However, many of these applications, such as autonomous driving and robotic …

Deep learning on point clouds has attracted increasing attention in the fields of 3D computer
vision and robotics. In particular, graph-based point-cloud deep neural networks (DNNs) …

3D shape generation is a fundamental operation in computer graphics. While significant
progress has been made, especially with recent deep generative models, it remains a …

Establishing correspondence between shapes is a very important and active research topic
in many domains. Due to the powerful ability of deep learning on geometric data, lots of …

3D shapes are widely used in computer graphics and computer vision, with applications
ranging from modeling, recognition to rendering. Synthesizing high-quality shapes is …

Many computer vision tasks address the problem of scene understanding and are naturally
interrelated eg object classification, detection, scene segmentation, depth estimation, etc …