411 research outputs found
Single-image RGB Photometric Stereo With Spatially-varying Albedo
We present a single-shot system to recover surface geometry of objects with
spatially-varying albedos, from images captured under a calibrated RGB
photometric stereo setup---with three light directions multiplexed across
different color channels in the observed RGB image. Since the problem is
ill-posed point-wise, we assume that the albedo map can be modeled as
piece-wise constant with a restricted number of distinct albedo values. We show
that under ideal conditions, the shape of a non-degenerate local constant
albedo surface patch can theoretically be recovered exactly. Moreover, we
present a practical and efficient algorithm that uses this model to robustly
recover shape from real images. Our method first reasons about shape locally in
a dense set of patches in the observed image, producing shape distributions for
every patch. These local distributions are then combined to produce a single
consistent surface normal map. We demonstrate the efficacy of the approach
through experiments on both synthetic renderings as well as real captured
images.Comment: 3DV 2016. Project page at http://www.ttic.edu/chakrabarti/rgbps
Depth Super-Resolution Meets Uncalibrated Photometric Stereo
A novel depth super-resolution approach for RGB-D sensors is presented. It
disambiguates depth super-resolution through high-resolution photometric clues
and, symmetrically, it disambiguates uncalibrated photometric stereo through
low-resolution depth cues. To this end, an RGB-D sequence is acquired from the
same viewing angle, while illuminating the scene from various uncalibrated
directions. This sequence is handled by a variational framework which fits
high-resolution shape and reflectance, as well as lighting, to both the
low-resolution depth measurements and the high-resolution RGB ones. The key
novelty consists in a new PDE-based photometric stereo regularizer which
implicitly ensures surface regularity. This allows to carry out depth
super-resolution in a purely data-driven manner, without the need for any
ad-hoc prior or material calibration. Real-world experiments are carried out
using an out-of-the-box RGB-D sensor and a hand-held LED light source.Comment: International Conference on Computer Vision (ICCV) Workshop, 201
Photometric Depth Super-Resolution
This study explores the use of photometric techniques (shape-from-shading and
uncalibrated photometric stereo) for upsampling the low-resolution depth map
from an RGB-D sensor to the higher resolution of the companion RGB image. A
single-shot variational approach is first put forward, which is effective as
long as the target's reflectance is piecewise-constant. It is then shown that
this dependency upon a specific reflectance model can be relaxed by focusing on
a specific class of objects (e.g., faces), and delegate reflectance estimation
to a deep neural network. A multi-shot strategy based on randomly varying
lighting conditions is eventually discussed. It requires no training or prior
on the reflectance, yet this comes at the price of a dedicated acquisition
setup. Both quantitative and qualitative evaluations illustrate the
effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(T-PAMI), 2019. First three authors contribute equall
Variational Uncalibrated Photometric Stereo under General Lighting
Photometric stereo (PS) techniques nowadays remain constrained to an ideal
laboratory setup where modeling and calibration of lighting is amenable. To
eliminate such restrictions, we propose an efficient principled variational
approach to uncalibrated PS under general illumination. To this end, the
Lambertian reflectance model is approximated through a spherical harmonic
expansion, which preserves the spatial invariance of the lighting. The joint
recovery of shape, reflectance and illumination is then formulated as a single
variational problem. There the shape estimation is carried out directly in
terms of the underlying perspective depth map, thus implicitly ensuring
integrability and bypassing the need for a subsequent normal integration. To
tackle the resulting nonconvex problem numerically, we undertake a two-phase
procedure to initialize a balloon-like perspective depth map, followed by a
"lagged" block coordinate descent scheme. The experiments validate efficiency
and robustness of this approach. Across a variety of evaluations, we are able
to reduce the mean angular error consistently by a factor of 2-3 compared to
the state-of-the-art.Comment: Haefner and Ye contributed equall
Polarimetric Multi-View Inverse Rendering
A polarization camera has great potential for 3D reconstruction since the
angle of polarization (AoP) and the degree of polarization (DoP) of reflected
light are related to an object's surface normal. In this paper, we propose a
novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering
(Polarimetric MVIR) that effectively exploits geometric, photometric, and
polarimetric cues extracted from input multi-view color-polarization images. We
first estimate camera poses and an initial 3D model by geometric reconstruction
with a standard structure-from-motion and multi-view stereo pipeline. We then
refine the initial model by optimizing photometric rendering errors and
polarimetric errors using multi-view RGB, AoP, and DoP images, where we propose
a novel polarimetric cost function that enables an effective constraint on the
estimated surface normal of each vertex, while considering four possible
ambiguous azimuth angles revealed from the AoP measurement. The weight for the
polarimetric cost is effectively determined based on the DoP measurement, which
is regarded as the reliability of polarimetric information. Experimental
results using both synthetic and real data demonstrate that our Polarimetric
MVIR can reconstruct a detailed 3D shape without assuming a specific surface
material and lighting condition.Comment: Paper accepted in IEEE Transactions on Pattern Analysis and Machine
Intelligence (2022). arXiv admin note: substantial text overlap with
arXiv:2007.0883
Towards Scalable Multi-View Reconstruction of Geometry and Materials
In this paper, we propose a novel method for joint recovery of camera pose,
object geometry and spatially-varying Bidirectional Reflectance Distribution
Function (svBRDF) of 3D scenes that exceed object-scale and hence cannot be
captured with stationary light stages. The input are high-resolution RGB-D
images captured by a mobile, hand-held capture system with point lights for
active illumination. Compared to previous works that jointly estimate geometry
and materials from a hand-held scanner, we formulate this problem using a
single objective function that can be minimized using off-the-shelf
gradient-based solvers. To facilitate scalability to large numbers of
observation views and optimization variables, we introduce a distributed
optimization algorithm that reconstructs 2.5D keyframe-based representations of
the scene. A novel multi-view consistency regularizer effectively synchronizes
neighboring keyframes such that the local optimization results allow for
seamless integration into a globally consistent 3D model. We provide a study on
the importance of each component in our formulation and show that our method
compares favorably to baselines. We further demonstrate that our method
accurately reconstructs various objects and materials and allows for expansion
to spatially larger scenes. We believe that this work represents a significant
step towards making geometry and material estimation from hand-held scanners
scalable
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