Search Results for author: Gopal Sharma

Found 6 papers, 3 papers with code

ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

2 code implementations ECCV 2020 Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, Radomír Měch

We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives.

Neural Shape Parsers for Constructive Solid Geometry

no code implementations22 Dec 2019 Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji

We investigate two architectures for this task --- a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack.

Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation

no code implementations3 Oct 2019 Gopal Sharma, Evangelos Kalogerakis, Subhransu Maji

We present a framework for learning representations of 3D shapes that reflect the information present in this meta data and show that it leads to improved generalization for semantic segmentation tasks.

Metric Learning Semantic Segmentation

Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks

no code implementations22 Dec 2018 Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum

In this paper, we instead use efficient truncated randomized search in this reward function to train structured prediction energy networks (SPENs), which provide efficient test-time inference using gradient-based search on a smooth, learned representation of the score landscape, and have previously yielded state-of-the-art results in structured prediction.

Structured Prediction

CSGNet: Neural Shape Parser for Constructive Solid Geometry

1 code implementation CVPR 2018 Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji

In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions.

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