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NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
Zihan Zhu*,
Songyou Peng*,
Viktor Larsson,
Weiwei Xu,
Hujun Bao,
Zhaopeng Cui,
Martin R. Oswald,
Marc Pollefeys
Conference on Computer Vision and Pattern Recognition (CVPR), 2022
(* equal contribution)
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A neural implicit-based RGB-D SLAM that can be applied to large-scale scenes.
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Shape As Points: A Differentiable Poisson Solver
Songyou Peng,
Chiyu "Max" Jiang,
Yiyi Liao,
Michael Niemeyer,
Marc Pollefeys,
Andreas Geiger
NeurIPS, 2021 (Oral, top 0.6%)
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An interpretable hybird shape representation that yields HQ watertight meshes at low inference times.
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UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
Michael Oechsle,
Songyou Peng,
Andreas Geiger
International Conference on Computer Vision (ICCV), 2021 (Oral, top 3%)
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Our method enables to reconstruct accurate surfaces without input masks.
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KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
Christian Reiser,
Songyou Peng,
Yiyi Liao,
Andreas Geiger
International Conference on Computer Vision (ICCV), 2021
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Over 2000x speed-ups for NeRF are possible by utilizing thousands of tiny MLPs.
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Dynamic Plane Convolutional Occupancy Networks
Stefan Lionar*,
Daniil Emtsev*,
Dusan Svilarkovic*,
Songyou Peng
Winter Conference on Applications of Computer Vision (WACV), 2021
(* equal contribution)
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A student project of 3D Vision course at ETH Zurich where I served as the advisor.
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Convolutional Occupancy Networks
Songyou Peng,
Michael Niemeyer,
Lars Mescheder,
Marc Pollefeys,
Andreas Geiger
European Conference on Computer Vision (ECCV), 2020 (Spotlight, top 5%)
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A flexible implicit representation for accurate large-scale 3D reconstruction.
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DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing
Shaohui Liu,
Yinda Zhang,
Songyou Peng,
Boxin Shi,
Marc Pollefeys,
Zhaopeng Cui
Conference on Computer Vision and Pattern Recognition (CVPR), 2020
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A differentiable renderer for deep implicit signed distance functions.
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Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
Songyou Peng and
Peter Sturm
International Conference on Computer Vision (ICCV), 2019 (Oral, top 4.6%)
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A novel system that interactively guides a user to take optimal calibration images.
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Photometric Depth Super-Resolution
Bjoern Haefner*, Songyou Peng*,
Alok Verma*,
Yvain Queau,
Daniel Cremers
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
(* equal contribution)
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Recover high-resolution depth maps with fine geometric details using photometric techniques.
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PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship
Le Zhang, Songyou Peng, Stefan Winkler
IEEE Transactions on Affective Computing (TAFFC), 2019. In press.
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A journal extension of our ACM MM 2018 paper.
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Give Me One Portrait Image, I Will Tell You Your Emotion and Personality
Songyou Peng, Le Zhang, Stefan Winkler, Marianne Winslett
ACM International Conference on Multimedia (ACM MM), 2018
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Technical Demo. A deep Siamese-like network is introduced to predict one's Big-Five personality and arousal-valence emotion from one portrait photo.
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Depth Super-Resolution Meets Uncalibrated Photometric Stereo
Songyou Peng, Bjoern Haefner, Yvain Queau, Daniel Cremers
International Conference on Computer Vision (ICCV) Workshops, 2017
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A novel depth super-resolution approach for RGB-D sensors is presented.
This paper a part of my master thesis, and subsumed by our TPAMI paper.
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High Quality Shape from a RGB-D Camera using Photometric Stereo
Songyou Peng
M.Sc. Thesis, Techinical University of Munich
Supervisor: Yvain Queau and Daniel Cremers
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