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publications
Leveraging Camera Calibration Transformers Model using Line Mixed Queries
Published in IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2024
This work was accepted in the Transformers for Vision (T4V) workshop. This project was improved and published in BMVC2024.
Dynamic 3D Scene Reconstruction from Classroom Videos
Published in IEEE Signal Processing Society 58th Asilomar Conference on Signals, Systems, and Computers, 2024
The paper describes the development of a system for estimating 3D speaker geometry from raw images of collaborative classroom videos. The proposed system integrates methods for 2D and 3D pose estimation with depth estimation and camera calibration to detect and reconstruct the 3D speaker geometry of a collaborative group of students. Results on the Human3.6M dataset show that the system can estimate 3D poses reasonably well without the need to pre-train on the Human3.6M dataset. Furthermore, for classroom videos, the proposed system outperformed a baseline approach trained on the Human3.6M dataset. The proposed system is used to provide the 3D speaker geometry to a new speaker diarization system that performs well in noisy classroom environments.
SOFI: Multi-Scale Deformable Transformer for Camera Calibration with Enhanced Line Queries
Published in The 35th British Machine Vision Conference 2024, 2024
Camera calibration consists of estimating camera parameters such as the zenith vanishing point and horizon line. Estimating the camera parameters allows other tasks like 3D rendering, artificial reality effects, and object insertion in an image. Transformer-based models have provided promising results; however, they lack cross-scale interaction. In this work, we introduce multi-Scale defOrmable transFormer for camera calibratIon with enhanced line queries, SOFI. SOFI improves the line queries used in CTRL-C and MSCC by using both line content and line geometric features. Moreover, SOFI’s line queries allow transformer models to adopt the multi-scale deformable attention mechanism to promote cross-scale interaction between the feature maps produced by the backbone. SOFI outperforms existing methods on the Google Street View, Horizon Line in the Wild, and Holicity datasets while keeping a competitive inference speed. Code is available at: https://github.com/SebastianJanampa/SOFI
DT-LSD: Deformable Transformer-based Line Segment Detection
Published in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025'', 2025
Line segment detection is a fundamental low-level task in computer vision, and improvements in this task can impact more advanced methods that depend on it. Most new methods developed for line segment detection are based on Convolutional Neural Networks (CNNs). Our paper seeks to address challenges that prevent the wider adoption of transformer-based methods for line segment detection. More specifically, we introduce a new model called Deformable Transformer-based Line Segment Detection (DT-LSD) that supports cross-scale interactions and can be trained quickly. This work proposes a novel Deformable Transformer-based Line Segment Detector (DT-LSD) that addresses LETR’s drawbacks. For faster training, we introduce Line Contrastive DeNoising (LCDN), a technique that stabilizes the one-to-one matching process and speeds up training by 34×. We show that DT-LSD is faster and more accurate than its predecessor transformer-based model (LETR) and outperforms all CNN-based models in terms of accuracy. In the Wireframe dataset, DT-LSD achieves 71.7 for sAP10 and 73.9 for sAP15; while 33.2 for sAP10 and 35.1 for sAP15 in the YorkUrban dataset. The code is available at https://github.com/SebastianJanampa/DT-LSD
teaching
Mentor for Physics 1 and Mathematics 1
Undergraduate course, Universidad de Ingenieria y Tecnologia, Department of Student Wellness, 2018
- Duties
- Teach Physics 1 and Mathematics 1 classes to students at academic risk.
- Period: April 2018 - May 2020
Teaching Assistant for Science Courses
Undergraduate course, Universidad de Ingenieria y Tecnologia, Department of Science, 2019
- Duties
- Clear students’ doubts during the exams.
- Period: Mar 2019 - Jul 2019
Teaching Assistant for ECE 131L Programming Fundamentals
Undergraduate course, The University of New Mexico, Department of Electrical & Computer Engineering, 2022
- Duties
- Teach programming concepts, including functions, arrays, pointers, and programming in the Linux environment.
- Combine digital image processing introductory topics with C programming.
- Period: Jan 2022 - Dec 2023