Hardik Jain

Hardik Jain

Research Scholar @ Technical University Berlin

Computer Vision and Remote Sensing Group

Latest Updates


GenIcoNet: Generative Icosahedral Mesh Convolutional Network

International Conference on 3D Vision (3DV), London, UK

Hardik Jain, Olaf Hellwich

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In the past few decades, the computer vision domain has achieved outstanding success in learning 3D shapes for classification, segmentation and image-based reconstruction. However, deep networks are less explored for the generative task of obtaining new 3D shapes from the learned representation. This problem becomes more prominent for 3D shapes represented as surface meshes, mainly because the mesh structure lacks regularity, an essential property for training deep generative networks. In this work, we remedy this problem by proposing a generative icosahedral mesh convolutional network (GenIcoNet) that learns data distribution of surface meshes. Our end-to-end trainable network learns semantic representations using 2D convolutional filters on the regularized icosahedral meshes. Our experiments demonstrate that both the architectures of GenIcoNet are able to outperform existing networks trained on intermediate surface mesh representations.

Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface

IEEE International Conference on Computer Vision Workshops (ICCVW), Korea, 2019

Hardik Jain, Manuel Wöllhaf, Olaf Hellwich

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In this work, we try to reconstruct 3D shape from images by using a parameterized representation of the shape. We perform iterative parameterization of the surface to obtain a planar representation. This representation is encoded with surface information to generate 2D geometry images, which can be conveniently learned using traditional deep neural networks without additional overhead. Our experiments demonstrate that the proposed network learns detailed features and is able to reconstruct geometrically accurate shapes from single image.

Improving 3D Face Geometry by Adapting Reconstruction From Stereo Image Pair to Generic Morphable Model

International Conference on Information Fusion (FUSION), Germany, 2016

Hardik Jain, Olaf Hellwich, R S Anand

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Stereo reconstruction from image pairs is a standard method for 3D acquisition of human faces. Depending on available imagery and accuracy requirements the resulting 3D reconstructions may have deficits. In this work we remedy such deficits combining the 3D stereo reconstruction with a generic Morphable Model. Prior shape information can be obtained by already developed methods, which uses landmarks to fit a morphable model to a single image resulting in a second 3D reconstruction. This alternative to the stereo reconstruction is combined with it, allowing to prefer information from the single image reconstruction whenever the stereo reconstruction shows untypical deviations from the expected 3D features of a human face.

Master's Thesis

Using Morphable Face Model to Improve Stereo Reconstruction and Visualising the Model on a Smartphone

IIT Roorkee, India (May 2016)

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Thesis Presentation

Stereo Reconstruction from image pair is a standard method for 3D acquisition of human faces. Stereo surface reconstruction has lots of holes, and limited texture information. In this work we remedy such deficits combining the 3D stereo reconstruction with a generic Morphable Face Model. A Major part of the thesis is devoted to improvement in stereo face reconstruction pipeline by allowing to prefer information from the single image reconstruction whenever the stereo reconstruction shows untypical deviations. From a pair of stereo images, one is used for single image reconstruction and the combination gives the stereo model. The two reconstruction are then combined to result in the deformed face model. The fusion outcome results in geometrically more accurate face reconstruction. Finally, the resultant deformed face model is visually presented on a smartphone using cardboard, which addresses the modern trend of low cost devices in virtual 3D visualization.

Accomplished Projects

Laser-Guided CT Intervention using Flexible Laser Bow

DGBMT Annual Conference on Biomedical Engineering, October 2021

Hardik Jain, Olaf Hellwich, Daniel Szymanski, Andreas Rose, Timo Krüger

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Laser bow systems are used for intervention guidance by steering laser rays. Such systems have shown applications in biopsy, nasal surgeries, drainage, pain therapy. In this paper, we introduce an image-based laser guided CT intervention. Instead of relying on traditional laser bow rigidly mounted to the CT scanner, our approach performs registration making use of a patient tracker in image as well as CT data to find the orientation of laser bow with respect to the CT data. The image is acquired by a camera rigidly connected to the laser bow. The proposed approach allows movability to the laser bow system without compromising on accuracy. Using camera allows real-time view of the intervention and can be used for futuristic applications

A Power Efficiency Enhancements of a Multi-Bit Accelerator for Memory Prohibitive Deep Neural Networks

IEEE Open Journal of Circuits and Systems, January 2021

Suhas Shivaprakash, Hardik Jain, Olaf Hellwich, Friedel Gerfers

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Journal Paper Conference Paper Conference Presentation

State of art deep neural network (DNN) models are both memory prohibitive and computationally intensive with millions of connections. Employing these models for an embedded mobile application is resource limited with large power consumption and significant bandwidth requirement. In this paper, we propose a power efficient multi-bit neural network accelerator, where we employ the technique of truncating the partial sum (PSum) results from the previous layer before feeding it into the next layer.

Patient Motion Compensation for Photogrammetric Registration

International Conference on Computer Vision Theory and Applications (VISAPP), Malta, Feb 2020

Hardik Jain, Olaf Hellwich, Andreas Rose, Nicholas Norman, Dirk Much, Timo Krüger

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In this work, we treat the task for multi-view monocular imagery acquiring both body surface as well as reference markers. To fulfill the high accuracy requirements the patient is not supposed to move while images are taken. An approach towards relaxing this demanding situation is to measure small movements of the patient, e.g. with help of an electromagnetic device, and to compensate for the measured motion prior to body surface triangulation. We present two approaches for motion compensation: disparity shift compensation, and moving cameras compensation - both capable of achieving patient registration qualitatively equivalent to motion-free registration.

Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents

Multimedia Tools and Applications (MTAP), Springer, Dec 2019

Hardik Jain, Sharad Joshi, Gaurav Gupta, Nitin Khanna

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In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a serious threat. Ability to easily and reliably identify source printer of a printed document can help a lot in reducing this menace.

An Enhanced Statistical Approach for Median Filtering Detection using Difference Image

IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), India, 2017

Hardik Jain, Joydeep Das, Hemant Kumar Verma, Nitin Khanna

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Paper Presentation

In image forensics, detection of image forgeries involving non-linear manipulations have received a great deal of interest in recent past. Median filtering (MF) is one such non-linear manipulation technique which is quite often used in number of applications such as to hide impulse noises. Unlike other linear filtering operations, non-linear characteristics of median filtering makes it harder to detect using traditional forensics methods designed for detecting linear operations.

VR Mobile Applications


Unity 3D, Blender, C#


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A virtual reality based application premiered on Long Night of Science 2019, Berlin Germany. This contains projects of computer vision and remote sensing group at Technical University Berlin, Germany. Scene includes 3D Toronto city model, dinosaur skeleton point cloud, train station reconstruction.

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This app illustrates the work of the seven technical committees of the Geoscience and Remote Sensing Society. Along with the seven scenes for each committee, the app contains an additional introduction scene to make users acquaint with the cardboard and its operations. Compatible with Google Cardboard V2.

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It is the first ever Human model based Virtual reality application on Google Play store. Apart from general C# scripting, design of this application also required creating animations for humanoid models. Main scene has three gift boxes enclosed with human models, which are triggered when reticle points it for more than 2 Seconds. Upon triggering, audio and animations start until the reticle points at the model. An easy switching between modes makes it suitable for full-screen view as well.