13 citations · 45 across the 10 of their papers we have counts for
11 papers
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
Abhinav Kumar, Yuliang Guo, Xinyu Huang +2
Monocular 3D detectors achieve remarkable performance on cars and smaller objects. However, their performance drops on larger objects, leading to fatal accidents. Some attribute th…
SUP-NeRF: A Streamlined Unification of Pose Estimation and NeRF for Monocular 3D Object Reconstruction
Yuliang Guo, Abhinav Kumar, Cheng Zhao +3
Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose. While gradient-based optimization in a NeRF framework updates the in…
PrObeD: Proactive Object Detection Wrapper
Vishal Asnani, Abhinav Kumar, Suya You +1
Previous research in object detection focuses on various tasks, including detecting objects in generic and camouflaged images. These works are regarded as passive works for ob…
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
Shengjie Zhu, Abhinav Kumar, Masa Hu +1
3D sensing for monocular in-the-wild images, e.g., depth estimation and 3D object detection, has become increasingly important. However, the unknown intrinsic parameter hinders the…
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…