2.1k citations · 2.3k across the 4 of their papers we have counts for
8 papers
ShapeGlot: Learning Language for Shape Differentiation
Panos Achlioptas, Judy Fan, Robert X. D. Hawkins +2
In this work we explore how fine-grained differences between the shapes of common objects are expressed in language, grounded on images and 3D models of the objects. We first build…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Charles R. Qi, Li Yi, Hao Su +1
Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by th…
GRASS: Generative Recursive Autoencoders for Shape Structures
Jun Li, Kai Xu, Siddhartha Chaudhuri +3
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively character…
Learning Hierarchical Shape Segmentation and Labeling from Online Repositories
Li Yi, Leonidas Guibas, Aaron Hertzmann +3
We propose a method for converting geometric shapes into hierarchically segmented parts with part labels. Our key idea is to train category-specific models from the scene graphs an…
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Charles R. Qi, Hao Su, Matthias Niessner +3
3D shape models are becoming widely available and easier to capture, making available 3D information crucial for progress in object classification. Current state-of-the-art methods…