1.2k citations · 2k across the 56 of their papers we have counts for
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CSGNet: Neural Shape Parser for Constructive Solid Geometry
Gopal Sharma, Rishabh Goyal, Difan Liu +2
We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive soli…
Reasoning about Fine-grained Attribute Phrases using Reference Games
Jong-Chyi Su, Chenyun Wu, Huaizu Jiang +1
We present a framework for learning to describe fine-grained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an ob…
Improved Bilinear Pooling with CNNs
Tsung-Yu Lin, Subhransu Maji
Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and their compact variants [10], have been shown to be effective at fine-grained recognition, scene catego…
Shape Generation using Spatially Partitioned Point Clouds
Matheus Gadelha, Subhransu Maji, Rui Wang
We propose a method to generate 3D shapes using point clouds. Given a point-cloud representation of a 3D shape, our method builds a kd-tree to spatially partition the points. This…
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis +2
We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense…