Christopher Zach

About

Christopher Zach is a leading researcher in computer vision and robotics, with key contributions in 3D perception, object detection, and visual localization. His work spans monocular 3D object detection, where he pioneered an end-to-end training approach using Intersection-over-Union loss (65 citations), enabling low-cost mobile robot perception from single images. Zach also advanced object pose recognition from range images through a dynamic programming framework (48 citations), addressing challenges in automated manufacturing. His stereo depth map fusion methods (46 citations) have been instrumental for robot navigation in indoor environments. Notable achievements include SPP-Net (38 citations), a deep learning approach for absolute pose regression using synthetic views, and an adaptive real-time visual SLAM system (11 citations) combining KLT tracking with wide baseline features. Zach's research consistently bridges theoretical innovation with practical robotics applications, making him a respected figure in the field.

Research Focus

Key Achievements

Top Papers

1. 1
   Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss

   65 citations · 2019
2. 2
   A dynamic programming approach for fast and robust object pose recognition from range images

   48 citations · 2015
3. 3
   Stereo depth map fusion for robot navigation

   46 citations · 2011
4. 4
   SPP-Net: Deep Absolute Pose Regression with Synthetic Views

   38 citations · 2017
5. 5
   Adaptive, real-time visual simultaneous localization and mapping

   11 citations · 2009
6. 6
   Stereo depth map fusion for robot navigation

   3 citations · 2011

Key Collaborators

Contact & Links