I am an Assistant Professor of Visual Computing and Computational Media at Texas A&M University College
Station. I primarily conduct research in the field of 3D computer vision, Visual AI, and Robotic
Automation. As a researcher, I am fascinated by how numerical construction can precisely represent
the perceptual concepts of images, such as 3D scene geometry, motions, lights, material, and
color. I aim to use these mathematical concepts to enable machines for a broader adoption using
visual data. My fascination led me to explore well-developed computing fields like computer
vision, artificial intelligence, computer graphics, and robotics. My research in computer vision
and computer graphics aims to introduce new methods for visual representation learning,
photogrammetry, and dynamic scene modeling. In AI and robotics, my research seeks to solve
real-world robotic automation problems by leveraging the benefits of deep neural networks in
learning visual representation and decision-making tasks.
Recent News
Recent Awards and Achievements
Current Students (Masters and Doctorate)
[Topic]: 3D Data-Acquisition.
[Topic]: Neural Rendering Methods.
[Topic]: Gesture Understanding.
[Topic]: Deep Reinforcement Learning.
[Topic]: 3D from Images for Motion Capture System.
[Topic]: Neural Rendering Methods.
Teaching Assistant and External Students
[Course]: VIST 172, Sec. 501-505
[Course]: VIST 172, Sec. 501-505
[Course]: VIST 172, Sec. 501-505
[Course]: VIST 172, Sec. 501-505
[Course]: VIST 172, Sec. 501-505
[Topic]: Image Retrieval
Selected Publications
Stereo Risk: A Continuous Modeling Approach to Stereo Matching
Authors : Ce Liu*, Suryansh Kumar*, Shuhang Gu, Radu Timofte, Yao Yao, Luc Van Gool. (Oral Presentation, Top 1.5% of the papers)*Equal Contribution
International Conference on Machine Learning (ICML), PMLR, 2024, Vienna, Austria
Project Website Topic: Stereo Matching, 3D Acquisition, Computer Vision.
ICGNet: A Unified Approach for Instance-Centric Grasping
Authors : René Zurbrügg, Yifan Liu, Francis Engelmann, Suryansh Kumar, Marco Hutter and others.International Conference on Robotics and Automation (ICRA), IEEE, 2024, Yokohama, Japan
Project Website Topic: Grasping, 3D Acquisition, Robotics.
Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images
Authors : Nishant Jain, Suryansh Kumar*, Luc Van Gool. (Oral Presentation, Invited)International Journal of Computer Vision (IJCV), Springer, 2024, IF: 19.5
Project Website Topic: View Synthesis, NeRF, Image-based Rendering.
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers
Authors: Junting Chen*, Guohao Li*, Suryansh Kumar, and others (*Equal Contribution)Robotics Science and Systems (RSS), RSS Foundation, 2023, Daegu, South Korea.
Project Website Topic: Embodied-AI, Active SLAM, Navigation, Vision and Language.
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
Authors: Ce Liu, Suryansh Kumar*, Shuhang Gu, Radu Timofte, Luc Van Gool. (Spotlight Oral Presentation)International Conference on Learning Representations (ICLR), 2023, Kigali, Rwanda.
Project Website Topic: 3D Computer Vision, Deep Learning.
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields
Authors: Soomin Lee*, Le Chen*, Jiahao Wang, Alex Liniger, Suryansh Kumar, and others (*Equal Contribution)IEEE RAL, International Conference on Robotics and Automation (ICRA), IEEE, 2023, London, UK. (Oral Presentation)
Project Website Topic: 3D Computer Vision, Robotics, Information Theory.
Uncertainty-Driven Dense Two-View Structure from Motion
Authors: Weirong Chen, Suryansh Kumar, and others (Oral Presentation)IEEE RAL, International Conference on Intelligent Robots and Systems (IROS), IEEE, 2023, Detroit, USA.
Project Website Topic: 3D Computer Vision, Deep Learning.
Organic Priors in Non-Rigid Structure from Motion
Authors: Suryansh Kumar, Luc Van Gool. (Oral Presentation, Top 2.7% of the papers)European Conference on Computer Vision (ECCV), 2022, Tel-Aviv, Israel.
Project Website Topic: 3D Computer Vision, Algebra, Geometry, Compressed Sensing.
A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Seg. of Indoor Scenes
Authors: Davide Menini, Suryansh Kumar*, and others. (Oral Presentation)IEEE RAL, International Conference on Robotics and Automation, (ICRA), IEEE, 2022, Philadelphia, USA.
Project Website Topic: 3D Computer Vision, Machine Learning.
Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure from Motion
Author: Suryansh Kumar. (Oral Presentation, Invited at Dynavis 2019)Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2019, CA, USA.
PDF Supplementary Video Topic: 3D Computer Vision, Matrix Manifolds