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Found 26 papers, 12 papers with code
NPLMV-PS: Neural Point-Light Multi-View Photometric Stereo
In this work we present a novel multi-view photometric stereo (MVPS) method.
VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field
However, in spite of high efficiency, APRs and SCRs have limited accuracy especially in large-scale outdoor scenes; HMs are accurate but need to store a large number of 2D descriptors for matching, resulting in poor efficiency.
PRAM: Place Recognition Anywhere Model for Efficient Visual Localization
Humans localize themselves efficiently in known environments by first recognizing landmarks defined on certain objects and their spatial relationships, and then verifying the location by aligning detailed structures of recognized objects with those in the memory.
Task Agnostic Architecture for Algorithm Induction via Implicit Composition
Third, the observation that the main missing component in developing a truly generalised network is an efficient approach for self-consistent input of previously learnt sub-steps of an algorithm and their (implicit) composition during the network's internal forward pass.
DiaLoc: An Iterative Approach to Embodied Dialog Localization
However, most existing works in embodied dialog research focus on navigation and leave the localization task understudied.
Function-constrained Program Synthesis
Generating computer programs in general-purpose programming languages like Python poses a challenge for LLMs when instructed to use code provided in the prompt.
A Neural Height-Map Approach for the Binocular Photometric Stereo Problem
As in recent neural multi-view shape estimation frameworks such as NeRF, SIREN and inverse graphics approaches to multi-view photometric stereo (e. g. PS-NeRF) we formulate shape estimation task as learning of a differentiable surface and texture representation by minimising surface normal discrepancy for normals estimated from multiple varying light images for two views as well as discrepancy between rendered surface intensity and observed images.
Sparse Multi-Object Render-and-Compare
Introducing a new network architecture Multi-SPARC we learn to perform CAD model alignments for multiple detected objects jointly.
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation
Monocular 3D human pose and shape estimation is an ill-posed problem since multiple 3D solutions can explain a 2D image of a subject.
Ranked #9 on
Multi-Hypotheses 3D Human Pose Estimation
on AH36M
IMP: Iterative Matching and Pose Estimation with Adaptive Pooling
Previous methods solve feature matching and pose estimation using a two-stage process by first finding matches and then estimating the pose.
A CNN Based Approach for the Point-Light Photometric Stereo Problem
Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered.
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 #42 on
Monocular Depth Estimation
on NYU-Depth V2
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.
Efficient Large-Scale Localization by Global Instance Recognition
Hierarchical frameworks consisting of both coarse and fine localization are often used as the standard pipeline for large-scale visual localization.
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.
Probabilistic Estimation of 3D Human Shape and Pose with a Semantic Local Parametric Model
This paper addresses the problem of 3D human body shape and pose estimation from RGB images.
Leveraging Geometry for Shape Estimation from a Single RGB Image
In this work we demonstrate how cross-domain keypoint matches from an RGB image to a rendered CAD model allow for more precise object pose predictions compared to ones obtained through direct predictions.
Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild
Thus, it is desirable to estimate a distribution over 3D body shape and pose conditioned on the input image instead of a single 3D reconstruction.
Ranked #1 on
3D Human Shape Estimation
on SSP-3D
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
LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo
In order to fill the gap in evaluating near-field photometric stereo methods, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of a varying of materials.
Probabilistic 3D Human Shape and Pose Estimation from Multiple Unconstrained Images in the Wild
In contrast, we propose a new task: shape and pose estimation from a group of multiple images of a human subject, without constraints on subject pose, camera viewpoint or background conditions between images in the group.
Ranked #3 on
3D Human Shape Estimation
on SSP-3D
3D Human Shape Estimation
Multi-Hypotheses 3D Human Pose Estimation
+1
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild
Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical body model) to overcome data scarcity.
Ranked #1 on
3D Human Shape Estimation
on MoVi
3D human pose and shape estimation
3D Human Shape Estimation
+3
A CNN Based Approach for the Near-Field Photometric Stereo Problem
Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration.
PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks
We show that global physical effects can be approximated on the observation map domain and this simplifies and speeds up the data creation procedure.
Large Scale Joint Semantic Re-Localisation and Scene Understanding via Globally Unique Instance Coordinate Regression
We obtain smaller mean distance and angular errors than state-of-the-art 6-DoF pose estimation algorithms based on direct pose regression and pose estimation from scene coordinates on all datasets.
