Papers
6
Total Citations
14
H-Index
2
About
Raphael Falque is a robotics researcher specializing in 3D perception, point cloud processing, and automation for agriculture and industrial applications. His work focuses on enabling robots to perceive and interact with dynamic, deformable environments—from livestock to meat processing to infrastructure inspection. Falque’s key contributions include developing semantic keypoint extraction for scanned animals using multi-depth-camera systems, a method that supports 3D reconstruction and tracking for livestock automation. He also pioneered real-time lidar-inertial motion correction and spatiotemporal dynamic object detection, advancing state estimation in cluttered scenes. His non-rigid point cloud registration technique, SPaM (Soft Patch Matching), improves accuracy for deformable objects, while VirtualButcher introduces coarse-to-fine annotation transfer for robotic meat cutting. More recently, Falque has explored vision transformers for obstruction classification in telecommunication pipes. With over 14 citations across his top papers, his work has practical impact in agricultural robotics, where automation can improve animal welfare and processing efficiency, and in infrastructure maintenance. Falque’s research bridges the gap between perception and real-world robotic manipulation in challenging, dynamic settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3SPaM: soft patch matching for non-rigid pointcloud registration2 citations · 2023
- 4
- 5Dynamic Object Detection in Range data using Spatiotemporal Normals2 citations · 2023
- 6