Enrique Dunn

About

Enrique Dunn is a leading researcher in computer vision and photogrammetry, with a focus on autonomous sensor planning and 3D reconstruction. His work centers on developing intelligent strategies for camera-equipped robots and vehicles to efficiently acquire geometric models of their environments. Dunn’s major contributions include pioneering methods for next-best-view planning, which optimize sensor placement to maximize accuracy while minimizing resource use—a critical advancement for autonomous navigation and mapping. His most cited paper, "Developing visual sensing strategies through next best view planning" (2009, 45 citations), introduces a structure-from-motion approach that enables goal-driven, efficient 3D scene modeling. He also advanced photogrammetric network design using evolutionary computing (2007, 15 citations), improving object point accuracy through optimized ray selection in bundle adjustments. Notable achievements include his multi-objective sensor planning framework (2004, 14 citations) and a geometric solver for calibrated stereo egomotion (2011, 7 citations), which enhances pose estimation for robotic systems balancing coverage and overlap. Dunn’s work has significantly impacted autonomous robotics and computer vision, offering practical solutions for real-world sensing challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   Developing visual sensing strategies through next best view planning

   45 citations · 2009
2. 2
   Development of a practical photogrammetric network design using evolutionary computing

   15 citations · 2007
3. 3
   Multi-objective Sensor Planning for Efficient and Accurate Object Reconstruction

   14 citations · 2004
4. 4
   A geometric solver for calibrated stereo egomotion

   7 citations · 2011
5. 5
   Evolutionary Computation for Sensor Planning: The Task Distribution Plan

   4 citations · 2003

Key Collaborators

Contact & Links