7 citations · 14 across the 7 of their papers we have counts for
4 papers · 1 filter
NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving
Alexander Popov, Patrik Gebhardt, Ke Chen +5
Detecting obstacles is crucial for safe and efficient autonomous driving. To this end, we present NVRadarNet, a deep neural network (DNN) that detects dynamic obstacles and drivabl…
Deep Two-View Structure-from-Motion Revisited
Jianyuan Wang, Yiran Zhong, Yuchao Dai +4
Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM. Existing deep learning-based approaches formulate the problem by either recovering abs…
MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views
Ke Chen, Ryan Oldja, Nikolai Smolyanskiy +5
Autonomous driving requires the inference of actionable information such as detecting and classifying objects, and determining the drivable space. To this end, we present Multi-Vie…
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield
We revisit the problem of visual depth estimation in the context of autonomous vehicles. Despite the progress on monocular depth estimation in recent years, we show that the gap be…