259 research outputs found
Polarimetric Pose Prediction
Light has many properties that vision sensors can passively measure.
Colour-band separated wavelength and intensity are arguably the most commonly
used for monocular 6D object pose estimation. This paper explores how
complementary polarisation information, i.e. the orientation of light wave
oscillations, influences the accuracy of pose predictions. A hybrid model that
leverages physical priors jointly with a data-driven learning strategy is
designed and carefully tested on objects with different levels of photometric
complexity. Our design significantly improves the pose accuracy compared to
state-of-the-art photometric approaches and enables object pose estimation for
highly reflective and transparent objects. A new multi-modal instance-level 6D
object pose dataset with highly accurate pose annotations for multiple objects
with varying photometric complexity is introduced as a benchmark.Comment: Accepted at ECCV 2022; 25 pages (14 main paper + References + 7
Appendix
Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Data
6D pose estimation pipelines that rely on RGB-only or RGB-D data show
limitations for photometrically challenging objects with e.g. textureless
surfaces, reflections or transparency. A supervised learning-based method
utilising complementary polarisation information as input modality is proposed
to overcome such limitations. This supervised approach is then extended to a
self-supervised paradigm by leveraging physical characteristics of polarised
light, thus eliminating the need for annotated real data. The methods achieve
significant advancements in pose estimation by leveraging geometric information
from polarised light and incorporating shape priors and invertible physical
constraints.Comment: Accepted at ICCV 2023 TRICKY Worksho
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
Learning-based methods to solve dense 3D vision problems typically train on
3D sensor data. The respectively used principle of measuring distances provides
advantages and drawbacks. These are typically not compared nor discussed in the
literature due to a lack of multi-modal datasets. Texture-less regions are
problematic for structure from motion and stereo, reflective material poses
issues for active sensing, and distances for translucent objects are intricate
to measure with existing hardware. Training on inaccurate or corrupt data
induces model bias and hampers generalisation capabilities. These effects
remain unnoticed if the sensor measurement is considered as ground truth during
the evaluation. This paper investigates the effect of sensor errors for the
dense 3D vision tasks of depth estimation and reconstruction. We rigorously
show the significant impact of sensor characteristics on the learned
predictions and notice generalisation issues arising from various technologies
in everyday household environments. For evaluation, we introduce a carefully
designed dataset\footnote{dataset available at
https://github.com/Junggy/HAMMER-dataset} comprising measurements from
commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular
RGB+P. Our study quantifies the considerable sensor noise impact and paves the
way to improved dense vision estimates and targeted data fusion.Comment: Accepted at CVPR 2023, Main Paper + Supp. Mat. arXiv admin note:
substantial text overlap with arXiv:2205.0456
Polarimetric Multi-View Inverse Rendering
A polarization camera has great potential for 3D reconstruction since the
angle of polarization (AoP) of reflected light is 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 and polarimetric rendering errors using
multi-view RGB and AoP images, where we propose a novel polarimetric rendering
cost function that enables us to effectively constrain each estimated surface
vertex's normal while considering four possible ambiguous azimuth angles
revealed from the AoP measurement. Experimental results using both synthetic
and real data demonstrate that our Polarimetric MVIR can reconstruct a detailed
3D shape without assuming a specific polarized reflection depending on the
material.Comment: Paper accepted in ECCV 202
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
Hyperspectral benthic mapping from underwater robotic platforms
We live on a planet of vast oceans; 70% of the Earth's surface is covered in water. They are integral to supporting life, providing 99% of the inhabitable space on Earth. Our oceans and the habitats within them are under threat due to a variety of factors. To understand the impacts and possible solutions, the monitoring of marine habitats is critically important. Optical imaging as a method for monitoring can provide a vast array of information however imaging through water is complex. To compensate for the selective attenuation of light in water, this thesis presents a novel light propagation model and illustrates how it can improve optical imaging performance. An in-situ hyperspectral system is designed which comprised of two upward looking spectrometers at different positions in the water column. The downwelling light in the water column is continuously sampled by the system which allows for the generation of a dynamic water model. In addition to the two upward looking spectrometers the in-situ system contains an imaging module which can be used for imaging of the seafloor. It consists of a hyperspectral sensor and a trichromatic stereo camera. New calibration methods are presented for the spatial and spectral co-registration of the two optical sensors. The water model is used to create image data which is invariant to the changing optical properties of the water and changing environmental conditions. In this thesis the in-situ optical system is mounted onboard an Autonomous Underwater Vehicle. Data from the imaging module is also used to classify seafloor materials. The classified seafloor patches are integrated into a high resolution 3D benthic map of the surveyed site. Given the limited imaging resolution of the hyperspectral sensor used in this work, a new method is also presented that uses information from the co-registered colour images to inform a new spectral unmixing method to resolve subpixel materials
Computational Imaging for Shape Understanding
Geometry is the essential property of real-world scenes. Understanding the shape of the object is critical to many computer vision applications. In this dissertation, we explore using computational imaging approaches to recover the geometry of real-world scenes. Computational imaging is an emerging technique that uses the co-designs of image hardware and computational software to expand the capacity of traditional cameras. To tackle face recognition in the uncontrolled environment, we study 2D color image and 3D shape to deal with body movement and self-occlusion. Especially, we use multiple RGB-D cameras to fuse the varying pose and register the front face in a unified coordinate system. The deep color feature and geodesic distance feature have been used to complete face recognition. To handle the underwater image application, we study the angular-spatial encoding and polarization state encoding of light rays using computational imaging devices. Specifically, we use the light field camera to tackle the challenging problem of underwater 3D reconstruction. We leverage the angular sampling of the light field for robust depth estimation. We also develop a fast ray marching algorithm to improve the efficiency of the algorithm. To deal with arbitrary reflectance, we investigate polarimetric imaging and develop polarimetric Helmholtz stereopsis that uses reciprocal polarimetric image pairs for high-fidelity 3D surface reconstruction. We formulate new reciprocity and diffuse/specular polarimetric constraints to recover surface depths and normals using an optimization framework. To recover the 3D shape in the unknown and uncontrolled natural illumination, we use two circularly polarized spotlights to boost the polarization cues corrupted by the environment lighting, as well as to provide photometric cues. To mitigate the effect of uncontrolled environment light in photometric constraints, we estimate a lighting proxy map and iteratively refine the normal and lighting estimation. Through expensive experiments on the simulated and real images, we demonstrate that our proposed computational imaging methods outperform traditional imaging approaches
Use of Depth Perception for the Improved Understanding of Hydrographic Data
This thesis has reviewed how increased depth perception can be used to increase the
understanding of hydrographic data First visual cues and various visual displays and
techniques were investigated. From this investigation 3D stereoscopic techniques prove to
be superior in improving the depth perception and understanding of spatially related data
and a further investigation on current 3D stereoscopic visualisation techniques was carried
out. After reviewing how hydrographic data is currently visualised it was decided that the
chromo stereoscopic visualisation technique is preferred to be used for further research on
selected hydrographic data models. A novel chromo stereoscopic application was
developed and the results from the evaluation on selected hydrographic data models clearly
show an improved depth perception and understanding of the data models
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