173 citations · 205 across the 8 of their papers we have counts for
14 papers
Finding Label and Model Errors in Perception Data With Learned Observation Assertions
Daniel Kang, Nikos Arechiga, Sudeep Pillai +2
ML is being deployed in complex, real-world scenarios where errors have impactful consequences. In these systems, thorough testing of the ML pipelines is critical. A key component…
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
Kuan-Hui Lee, Matthew Kliemann, Adrien Gaidon +4
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particula…
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion
Igor Vasiljevic, Vitor Guizilini, Rares Ambrus +4
Self-supervised learning has emerged as a powerful tool for depth and ego-motion estimation, leading to state-of-the-art results on benchmark datasets. However, one significant lim…
Neural Outlier Rejection for Self-Supervised Keypoint Learning
Jiexiong Tang, Hanme Kim, Vitor Guizilini +2
Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms. Recently, several learned keypoint methods have demonstra…
Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
Jiexiong Tang, Rares Ambrus, Vitor Guizilini +4
Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion. State-of-the-art learning-based methods generate training samples…
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances
Vitor Guizilini, Jie Li, Rares Ambrus +2
Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression…