74 citations · 147 across the 8 of their papers we have counts for
16 papers
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
Yue Wang, Vitor Guizilini, Tianyuan Zhang +3
We introduce a framework for multi-camera 3D object detection. In contrast to existing works, which estimate 3D bounding boxes directly from monocular images or use depth predictio…
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Dennis Park, Rares Ambrus, Vitor Guizilini +2
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors. These…
Full Surround Monodepth from Multiple Cameras
Vitor Guizilini, Igor Vasiljevic, Rares Ambrus +2
Self-supervised monocular depth and ego-motion estimation is a promising approach to replace or supplement expensive depth sensors such as LiDAR for robotics applications like auto…
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
Vitor Guizilini, Rares Ambrus, Wolfram Burgard +1
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from…
Geometric Unsupervised Domain Adaptation for Semantic Segmentation
Vitor Guizilini, Jie Li, Rares Ambrus +1
Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a d…
Monocular Depth Estimation for Soft Visuotactile Sensors
Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy +3
Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key challenges for robust manipulation, as they enable reliable grasps along with the ability to obtain hi…