N. Mitra

Publications
Influence

Global contrast based salient region detection

Ming-Ming Cheng, Guo-Xin Zhang, N. Mitra, Xiaolei Huang, S. Hu
Computer Science
CVPR
20 June 2011

We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. Expand

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Super 4PCS Fast Global Pointcloud Registration via Smart Indexing

Nicolas Mellado, Dror Aiger, N. Mitra
Computer Science
Comput. Graph. Forum
9 July 2014

We present SUPER 4PCS, a fast global registration for pointsets, which runs in optimal linear time and is output sensitive in the complexity of the alignment problem based on the (unknown) overlap across scan pairs. Expand

Robust global registration

N. Gelfand, N. Mitra, L. Guibas, H. Pottmann
Mathematics, Computer Science
SGP '05
4 July 2005

We present an algorithm for the automatic alignment of two 3D shapes (data and model), without any assumptions about their initial positions. Expand

Partial and approximate symmetry detection for 3D geometry

N. Mitra, L. Guibas, M. Pauly
Computer Science
SIGGRAPH '06
2006

This paper presents a new algorithm that processes geometric models and efficiently discovers and extracts a compact representation of their Euclidean symmetries. Expand

SalientShape: group saliency in image collections

Ming-Ming Cheng, N. Mitra, Xiaolei Huang, S. Hu
Computer Science
The Visual Computer
1 April 2014

We propose a simple, fast, and effective algorithm for locating and segmenting salient objects in large image collections by analysing image collections. Expand

Geometric modeling in shape space

M. Kilian, N. Mitra, H. Pottmann
Mathematics
SIGGRAPH
29 July 2007

We present a novel framework to treat shapes in the setting of Riemannian geometry as points in a shape space to aid the user in design and modeling tasks, especially to explore the space of deformations of a given shape. Expand

PCPNet Learning Local Shape Properties from Raw Point Clouds

P. Guerrero, Yanir Kleiman, M. Ovsjanikov, N. Mitra
Computer Science, Mathematics
Comput. Graph. Forum
13 October 2017

In this paper, we propose PCPNET, a deep‐learning based approach for estimating local 3D shape properties in point clouds. Expand

Discovering structural regularity in 3D geometry

M. Pauly, N. Mitra, J. Wallner, H. Pottmann, L. Guibas
Computer Science
SIGGRAPH
1 August 2008

We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. Expand

Registration of point cloud data from a geometric optimization perspective

N. Mitra, N. Gelfand, H. Pottmann, L. Guibas
Mathematics, Computer Science
SGP '04
8 July 2004

We propose a framework for pairwise registration of shapes represented by point cloud data (PCD). Expand

Estimating surface normals in noisy point cloud data

N. Mitra, A. T. Nguyen, L. Guibas
Mathematics, Computer Science
Int. J. Comput. Geom. Appl.
1 October 2004

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. Expand

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