Search Results for author:
Found 9 papers, 7 papers with code
U-ARE-ME: Uncertainty-Aware Rotation Estimation in Manhattan Environments
Given a sequence of images, we can use the per-frame rotation estimates and their uncertainty to perform multi-frame optimisation, achieving robustness and temporal consistency.
Rethinking Inductive Biases for Surface Normal Estimation
Despite the growing demand for accurate surface normal estimation models, existing methods use general-purpose dense prediction models, adopting the same inductive biases as other tasks.
SuperPrimitive: Scene Reconstruction at a Primitive Level
We address this issue with a new image representation which we call a SuperPrimitive.
FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic Data
Our FOUND approach tackles this, with 4 main contributions: (i) SynFoot, a synthetic dataset of 50, 000 photorealistic foot images, paired with ground truth surface normals and keypoints; (ii) an uncertainty-aware surface normal predictor trained on our synthetic dataset; (iii) an optimization scheme for fitting a generative foot model to a series of images; and (iv) a benchmark dataset of calibrated images and high resolution ground truth geometry.
IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty
The depth of each pixel can be propagated to a query pixel, using the predicted surface normal as guidance.
Ranked #36 on
Monocular Depth Estimation
on NYU-Depth V2
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet.
Ranked #2 on
Synthetic Face Recognition
on CPLFW
(Accuracy metric)
SPARC: Sparse Render-and-Compare for CAD model alignment in a single RGB image
This combined information is the input to a pose prediction network, SPARC-Net which we train to predict a 9 DoF CAD model pose update.
Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry
To this end, we propose MaGNet, a novel framework for fusing single-view depth probability with multi-view geometry, to improve the accuracy, robustness and efficiency of multi-view depth estimation.
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
Experimental results show that the proposed method outperforms the state-of-the-art in ScanNet and NYUv2, and that the estimated uncertainty correlates well with the prediction error.
Ranked #1 on
Surface Normal Estimation
on NYU-Depth V2
