Search Results for author: Iro Armeni

Found 6 papers, 3 papers with code

ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields

1 code implementation24 Jan 2022 Corinne Stucker, Bingxin Ke, Yuanwen Yue, Shengyu Huang, Iro Armeni, Konrad Schindler

To make full use of the point cloud and the underlying images, we introduce ImpliCity, a neural representation of the 3D scene as an implicit, continuous occupancy field, driven by learned embeddings of the point cloud and a stereo pair of ortho-photos.

3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera

1 code implementation ICCV 2019 Iro Armeni, Zhi-Yang He, JunYoung Gwak, Amir R. Zamir, Martin Fischer, Jitendra Malik, Silvio Savarese

Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e. g., class, material, and other attributes), rooms (e. g., scene category, volume, etc.)

SEGCloud: Semantic Segmentation of 3D Point Clouds

no code implementations20 Oct 2017 Lyne P. Tchapmi, Christopher B. Choy, Iro Armeni, JunYoung Gwak, Silvio Savarese

Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation.

Joint 2D-3D-Semantic Data for Indoor Scene Understanding

2 code implementations3 Feb 2017 Iro Armeni, Sasha Sax, Amir R. Zamir, Silvio Savarese

We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2. 5D and 3D domains, with instance-level semantic and geometric annotations.

Scene Understanding

3D Semantic Parsing of Large-Scale Indoor Spaces

no code implementations CVPR 2016 Iro Armeni, Ozan Sener, Amir R. Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, Silvio Savarese

In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e. g. rooms, etc) that are aligned into a canonical reference coordinate system.

Semantic Parsing

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