Search Results for author:
Found 18 papers, 4 papers with code
NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature.
General Planar Motion from a Pair of 3D Correspondences
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.
Geometric Viewpoint Learning with Hyper-Rays and Harmonics Encoding
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.
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization
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.
LASER: LAtent SpacE Rendering for 2D Visual Localization
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.
GTT-Net: Learned Generalized Trajectory Triangulation
We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry.
VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough
We present a dense-indirect SLAM system using external dense optical flows as input.
VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals
We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences.
Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction
We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution.
Learned Contextual Feature Reweighting for Image Geo-Localization
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.
Self-expressive Dictionary Learning for Dynamic 3D Reconstruction
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).
Minimal Solvers for 3D Geometry From Satellite Imagery
We propose two novel minimal solvers which advance the state of the art in satellite imagery processing.
Sparse Dynamic 3D Reconstruction From Unsynchronized Videos
We target the sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap.
Synthesizing Illumination Mosaics From Internet Photo-Collections
We propose a framework for the automatic creation of time-lapse mosaics of a given scene.
Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD
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.
Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset)
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.
PatchMatch Based Joint View Selection and Depthmap Estimation
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.
Stereo under Sequential Optimal Sampling: A Statistical Analysis Framework for Search Space Reduction
We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model.
