Erik Bylow

About

Erik Bylow is a leading researcher in real-time 3D computer vision, with a primary focus on camera tracking, dense 3D reconstruction, and the use of signed distance functions (SDFs). His most influential work, the 2013 paper "Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions," has garnered over 200 citations and introduced a novel method for rapidly acquiring 3D models of static indoor environments using RGB-D sensors. This work addressed a critical need across robotics, computer vision, geodesy, and architecture by enabling efficient, real-time performance. Bylow further advanced the field by developing direct camera pose tracking and mapping techniques that leverage SDFs for textured environments, and by combining depth sensor data with sparse feature points to enhance the robustness of online 3D reconstruction. His contributions have been instrumental in making dense, real-time 3D modeling more practical and reliable, particularly following the release of the Microsoft Kinect. Bylow’s research continues to influence how dynamic and static scenes are captured and understood in real-time applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions

   204 citations · 2013
2. 2
   Direct Camera Pose Tracking and Mapping With Signed Distance Functions

   8 citations · 2013
3. 3
   Robust online 3D reconstruction combining a depth sensor and sparse feature points

   8 citations · 2016

Key Collaborators

Contact & Links