Search Results for author:
Found 9 papers, 9 papers with code
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not.
3D Object Detection From Monocular Images
Monocular 3D Object Detection
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e. g. urban driving scenes.
3D Object Detection
3D Object Detection From Monocular Images
+2
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
In this paper, we present and integrate GrooMeD-NMS -- a novel Grouped Mathematically Differentiable NMS for monocular 3D object detection, such that the network is trained end-to-end with a loss on the boxes after NMS.
Ranked #10 on
3D Object Detection From Monocular Images
on KITTI-360
3D Object Detection From Monocular Images
Monocular 3D Object Detection
+2
Kinematic 3D Object Detection in Monocular Video
In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.
Ranked #6 on
3D Object Detection
on Rope3D
The Edge of Depth: Explicit Constraints between Segmentation and Depth
In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images.
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
Understanding the world in 3D is a critical component of urban autonomous driving.
3D Object Detection From Monocular Images
Autonomous Driving
+5
Pedestrian Detection with Autoregressive Network Phases
We present an autoregressive pedestrian detection framework with cascaded phases designed to progressively improve precision.
Recurrent Flow-Guided Semantic Forecasting
Understanding the world around us and making decisions about the future is a critical component to human intelligence.
Illuminating Pedestrians via Simultaneous Detection & Segmentation
When placed properly, the additional supervision helps guide features in shared layers to become more sophisticated and helpful for the downstream pedestrian detector.
Ranked #20 on
Pedestrian Detection
on Caltech
