Konversatorium on Friday, June 22, 2018 - 10:30

Slots for Talks still available! Please contact the KV administration.
Friday, June 22, 2018 - 10:30
Seminar room 193-2 (Favoritenstraße 9, Stiege 1, 5th floor)

Introductory Talk (internship with CG group)

Jean-Baptiste Daval (Inst. 193-02 CG)
10 + 5
Jean-Baptiste Daval studies computer science at ENS Cachan. He will be realizing his bachelor thesis by an internship with the Rendering and Modeling group where he will be supervised by Stefan Ohrhallinger.
His topic is "Skype 3D": Video conferencing (e.g. Skype) records and displays flat, static image frames. In order to provide a more realistic (plastic and dynamic) appearance, the viewer should be able to move their head a bit 'around' the other person. This requires tracking the viewer's head's location, recording the viewee in stereo, e.g. Kinect depth images and reproject the input point cloud according to the viewer's position. See here for a headtracking example: https://www.youtube.com/watch?v=4s51IIjSQsw

FitConnect: Connecting Noisy 2D Samples by Fitted Neighborhoods ( SGP'18 Test talk)

Stefan Ohrhallinger (Inst. 193-02 CG)
20 + 10

We propose a parameter-free method to recover manifold connectivity in unstructured 2D point clouds with high noise in terms of the local feature size. This enables us to capture the features which emerge out of the noise. To achieve this, we extend the reconstruction algorithm HNN-C RUST , which connects samples to two (noise-free) neighbors and has been proven to output a manifold for a relaxed sampling condition. Applying this condition to noisy samples by projecting their k-nearest neighborhoods onto local circular fits leads to multiple candidate neighbor pairs and thus makes connecting them consistently an NP-hard problem. To solve this efficiently, we design an algorithm that searches that solution space iteratively on different scales of k. It achieves linear time complexity in terms of point count plus quadratic time in the size of noise clusters. Our algorithm FITCONNECT extends HNN-CRUST seamlessly to connect both samples with and without noise, performs as local as the recovered features and can output multiple open or closed piece-wise curves. Incidentally, our method simplifies the output geometry by eliminating all but a representative point from noisy clusters. Since local neighborhood fits overlap consistently, the resulting connectivity represents an ordering of the samples along a manifold. This permits us to simply blend the local fits for denoising with the locally estimated noise extent. Aside from applications like reconstructing silhouettes of noisy sensed data, this lays important groundwork to improve surface reconstruction in 3D. Our open-source algorithm is available online.