4 citations · 4 across the 7 of their papers we have counts for
10 papers
Scalable and Differentiable Point-Cloud Registration Using Maximum Mean Discrepancy
Rixon Crane, Fahira Afzal Maken, Nicholas Lawrance +4
We present MMD-Reg, a novel correspondence-free approach to point-cloud registration that is differentiable and has linear computational complexity in the number of points. We mode…
A Hybrid Optimization Framework for Grasp Synthesis under Partial Observations
Wenzheng Zhang, Fahira Afzal Maken, Tin Lai +1
We propose a hybrid grasp synthesis framework that combines a learning-based Energy-Based Model (EBM) with an analytical Iterative Closest Point (ICP) method to generate robust gra…
GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction
Jinguang Tong, Xuesong li, Fahira Afzal Maken +4
3D modeling of highly reflective objects remains challenging due to strong view-dependent appearances. While previous SDF-based methods can recover high-quality meshes, they are of…
Grasping by parallel shape matching
Wenzheng Zhang, Fahira Afzal Maken, Tin Lai +1
Grasping is essential in robotic manipulation, yet challenging due to object and gripper diversity and real-world complexities. Traditional analytic approaches often have long opti…
Homography Guided Temporal Fusion for Road Line and Marking Segmentation
Shan Wang, Chuong Nguyen, Jiawei Liu +6
Reliable segmentation of road lines and markings is critical to autonomous driving. Our work is motivated by the observations that road lines and markings are (1) frequently occlud…
Seeing Through the Glass: Neural 3D Reconstruction of Object Inside a Transparent Container
Jinguang Tong, Sundaram Muthu, Fahira Afzal Maken +2
In this paper, we define a new problem of recovering the 3D geometry of an object confined in a transparent enclosure. We also propose a novel method for solving this challenging p…
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Estimation of a dynamical system's latent state subject to sensor noise and model inaccuracies remains a critical yet difficult problem in robotics. While Kalman filters provide th…
Stein ICP for Uncertainty Estimation in Point Cloud Matching
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Quantification of uncertainty in point cloud matching is critical in many tasks such as pose estimation, sensor fusion, and grasping. Iterative closest point (ICP) is a commonly us…
Estimating Motion Uncertainty with Bayesian ICP
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Accurate uncertainty estimation associated with the pose transformation between two 3D point clouds is critical for autonomous navigation, grasping, and data fusion. Iterative clos…
Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation. Aligning point clouds produ…