69 citations · 98 across the 7 of their papers we have counts for
9 papers
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
Abhinav Kumar, Garrick Brazil, Enrique Corona +2
Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations. However, these vanilla blocks are not equivariant to arbitrary 3D…
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
Garrick Brazil, Abhinav Kumar, Julian Straub +3
Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and s…
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
Abhinav Kumar, Garrick Brazil, Xiaoming Liu
Modern 3D object detectors have immensely benefited from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) on…
Kinematic 3D Object Detection in Monocular Video
Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu +1
Perceiving the physical world in 3D is fundamental for self-driving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and de…
The Edge of Depth: Explicit Constraints between Segmentation and Depth
Shengjie Zhu, Garrick Brazil, Xiaoming Liu
In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images. For example, to help unsuperv…
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
Garrick Brazil, Xiaoming Liu
Understanding the world in 3D is a critical component of urban autonomous driving. Generally, the combination of expensive LiDAR sensors and stereo RGB imaging has been paramount f…
Pedestrian Detection with Autoregressive Network Phases
Garrick Brazil, Xiaoming Liu
We present an autoregressive pedestrian detection framework with cascaded phases designed to progressively improve precision. The proposed framework utilizes a novel lightweight st…
Recurrent Flow-Guided Semantic Forecasting
Adam M. Terwilliger, Garrick Brazil, Xiaoming Liu
Understanding the world around us and making decisions about the future is a critical component to human intelligence. As autonomous systems continue to develop, their ability to r…
Illuminating Pedestrians via Simultaneous Detection & Segmentation
Garrick Brazil, Xi Yin, Xiaoming Liu
Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can…