no code implementations • CVPR 2023 • Zhixiang Min, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Enrique Dunn, Manmohan Chandraker
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature.
no code implementations • ICCV 2023 • Juan Carlos Dibene, Zhixiang Min, Enrique Dunn
Instead, we enforce geometric constraints identifying, in closed-form, a unique planar motion solution from an orbital set of geometrically consistent SE(3) motion estimates.
no code implementations • ICCV 2023 • Zhixiang Min, Juan Carlos Dibene, Enrique Dunn
1) We propose a generalized viewpoint representation forgoing the analysis of photometric pixels in favor of encoded viewing ray embeddings attained from point cloud learning frameworks.
1 code implementation • 8 Dec 2022 • Xiangyu Xu, Li Guan, Enrique Dunn, Haoxiang Li, Gang Hua
In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization.
1 code implementation • CVPR 2022 • Zhixiang Min, Naji Khosravan, Zachary Bessinger, Manjunath Narayana, Sing Bing Kang, Enrique Dunn, Ivaylo Boyadzhiev
LASER introduces the concept of latent space rendering, where 2D pose hypotheses on the floor map are directly rendered into a geometrically-structured latent space by aggregating viewing ray features.
no code implementations • ICCV 2021 • Xiangyu Xu, Enrique Dunn
We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry.
1 code implementation • 14 Apr 2021 • Zhixiang Min, Enrique Dunn
We present a dense-indirect SLAM system using external dense optical flows as input.
1 code implementation • CVPR 2020 • Zhixiang Min, Yiding Yang, Enrique Dunn
We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences.
no code implementations • ICCV 2019 • Xiangyu Xu, Enrique Dunn
We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution.
no code implementations • CVPR 2017 • Hyo Jin Kim, Enrique Dunn, Jan-Michael Frahm
We address the problem of large scale image geo-localization where the location of an image is estimated by identifying geo-tagged reference images depicting the same place.
no code implementations • 22 May 2016 • Enliang Zheng, Dinghuang Ji, Enrique Dunn, Jan-Michael Frahm
Given the smooth motion of dynamic objects, we observe any element in the dictionary can be well approximated by a sparse linear combination of other elements in the same dictionary (i. e. self-expression).
no code implementations • ICCV 2015 • Enliang Zheng, Ke Wang, Enrique Dunn, Jan-Michael Frahm
We propose two novel minimal solvers which advance the state of the art in satellite imagery processing.
no code implementations • ICCV 2015 • Enliang Zheng, Dinghuang Ji, Enrique Dunn, Jan-Michael Frahm
We target the sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap.
no code implementations • ICCV 2015 • Dinghuang Ji, Enrique Dunn, Jan-Michael Frahm
We propose a framework for the automatic creation of time-lapse mosaics of a given scene.
no code implementations • ICCV 2015 • Hyo Jin Kim, Enrique Dunn, Jan-Michael Frahm
We address the problem of recognizing a place depicted in a query image by using a large database of geo-tagged images at a city-scale.
no code implementations • CVPR 2015 • Jared Heinly, Johannes L. Schonberger, Enrique Dunn, Jan-Michael Frahm
We propose a novel, large-scale, structure-from-motion framework that advances the state of the art in data scalability from city-scale modeling (millions of images) to world-scale modeling (several tens of millions of images) using just a single computer.
no code implementations • CVPR 2014 • Yilin Wang, Ke Wang, Enrique Dunn, Jan-Michael Frahm
We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model.
no code implementations • CVPR 2014 • Enliang Zheng, Enrique Dunn, Vladimir Jojic, Jan-Michael Frahm
We propose a multi-view depthmap estimation approach aimed at adaptively ascertaining the pixel level data associations between a reference image and all the elements of a source image set.