Shape recovery from reflection.

by Yingli Tian.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 202-222).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Physics-Based Shape Recovery Techniques --- p.3Chapter 1.2 --- Proposed Approaches to Shape Recovery in this Thesis --- p.9Chapter 1.3 --- Thesis Outline --- p.13Chapter 2 --- Camera Model in Color Vision --- p.15Chapter 2.1 --- Introduction --- p.15Chapter 2.2 --- Spectral Linearization --- p.17Chapter 2.3 --- Image Balancing --- p.21Chapter 2.4 --- Spectral Sensitivity --- p.24Chapter 2.5 --- Color Clipping and Blooming --- p.24Chapter 3 --- Extended Light Source Models --- p.27Chapter 3.1 --- Introduction --- p.27Chapter 3.2 --- A Spherical Light Model in 2D Coordinate System --- p.30Chapter 3.2.1 --- Basic Photometric Function for Hybrid Surfaces under a Point Light Source --- p.32Chapter 3.2.2 --- Photometric Function for Hybrid Surfaces under the Spher- ical Light Source --- p.34Chapter 3.3 --- A Spherical Light Model in 3D Coordinate System --- p.36Chapter 3.3.1 --- Radiance of the Spherical Light Source --- p.36Chapter 3.3.2 --- Surface Brightness Illuminated by One Point of the Spher- ical Light Source --- p.38Chapter 3.3.3 --- Surface Brightness Illuminated by the Spherical Light Source --- p.39Chapter 3.3.4 --- Rotating the Source-Object Coordinate to the Camera- Object Coordinate --- p.41Chapter 3.3.5 --- Surface Reflection Model --- p.44Chapter 3.4 --- Rectangular Light Model in 3D Coordinate System --- p.45Chapter 3.4.1 --- Radiance of a Rectangular Light Source --- p.45Chapter 3.4.2 --- Surface Brightness Illuminated by One Point of the Rect- angular Light Source --- p.47Chapter 3.4.3 --- Surface Brightness Illuminated by a Rectangular Light Source --- p.47Chapter 4 --- Shape Recovery from Specular Reflection --- p.54Chapter 4.1 --- Introduction --- p.54Chapter 4.2 --- Theory of the First Method --- p.57Chapter 4.2.1 --- Torrance-Sparrow Reflectance Model --- p.57Chapter 4.2.2 --- Relationship Between Surface Shapes from Different Images --- p.60Chapter 4.3 --- Theory of the Second Method --- p.65Chapter 4.3.1 --- Getting the Depth of a Reference Point --- p.65Chapter 4.3.2 --- Recovering the Depth and Normal of a Specular Point Near the Reference Point --- p.67Chapter 4.3.3 --- Recovering Local Shape of the Object by Specular Reflection --- p.69Chapter 4.4 --- Experimental Results and Discussions --- p.71Chapter 4.4.1 --- Experimental System and Results of the First Method --- p.71Chapter 4.4.2 --- Experimental System and Results of the Second Method --- p.76Chapter 5 --- Shape Recovery from One Sequence of Color Images --- p.81Chapter 5.1 --- Introduction --- p.81Chapter 5.2 --- Temporal-color Space Analysis of Reflection --- p.84Chapter 5.3 --- Estimation of Illuminant Color Ks --- p.88Chapter 5.4 --- Estimation of the Color Vector of the Body-reflection Component Kl --- p.89Chapter 5.5 --- Separating Specular and Body Reflection Components and Re- covering Surface Shape and Reflectance --- p.91Chapter 5.6 --- Experiment Results and Discussions --- p.92Chapter 5.6.1 --- Results with Interreflection --- p.93Chapter 5.6.2 --- Results Without Interreflection --- p.93Chapter 5.6.3 --- Simulation Results --- p.95Chapter 5.7 --- Analysis of Various Factors on the Accuracy --- p.96Chapter 5.7.1 --- Effects of Number of Samples --- p.96Chapter 5.7.2 --- Effects of Noise --- p.99Chapter 5.7.3 --- Effects of Object Size --- p.99Chapter 5.7.4 --- Camera Optical Axis Not in Light Source Plane --- p.102Chapter 5.7.5 --- Camera Optical Axis Not Passing Through Object Center --- p.105Chapter 6 --- Shape Recovery from Two Sequences of Images --- p.107Chapter 6.1 --- Introduction --- p.107Chapter 6.2 --- Method for 3D Shape Recovery from Two Sequences of Images --- p.109Chapter 6.3 --- Genetics-Based Method --- p.111Chapter 6.4 --- Experimental Results and Discussions --- p.115Chapter 6.4.1 --- Simulation Results --- p.115Chapter 6.4.2 --- Real Experimental Results --- p.118Chapter 7 --- Shape from Shading for Non-Lambertian Surfaces --- p.120Chapter 7.1 --- Introduction --- p.120Chapter 7.2 --- Reflectance Map for Non-Lambertian Color Surfaces --- p.123Chapter 7.3 --- Recovering Non-Lambertian Surface Shape from One Color Image --- p.127Chapter 7.3.1 --- Segmenting Hybrid Areas from Diffuse Areas Using Hue Information --- p.127Chapter 7.3.2 --- Calculating Intensities of Specular and Diffuse Compo- nents on Hybrid Areas --- p.128Chapter 7.3.3 --- Recovering Shape from Shading --- p.129Chapter 7.4 --- Experimental Results and Discussions --- p.131Chapter 7.4.1 --- Simulation Results --- p.131Chapter 7.4.2 --- Real Experimental Results --- p.136Chapter 8 --- Shape from Shading under Multiple Extended Light Sources --- p.142Chapter 8.1 --- Introduction --- p.142Chapter 8.2 --- Reflectance Map for Lambertian Surface Under Multiple Rectan- gular Light Sources --- p.144Chapter 8.3 --- Recovering Surface Shape Under Multiple Rectangular Light Sources --- p.148Chapter 8.4 --- Experimental Results and Discussions --- p.150Chapter 8.4.1 --- Synthetic Image Results --- p.150Chapter 8.4.2 --- Real Image Results --- p.152Chapter 9 --- Shape from Shading in Unknown Environments by Neural Net- works --- p.167Chapter 9.1 --- Introduction --- p.167Chapter 9.2 --- Shape Estimation --- p.169Chapter 9.2.1 --- Shape Recovery Problem under Multiple Rectangular Ex- tended Light Sources --- p.169Chapter 9.2.2 --- Forward Network Representation of Surface Normals --- p.170Chapter 9.2.3 --- Shape Estimation --- p.174Chapter 9.3 --- Application of the Neural Network in Shape Recovery --- p.174Chapter 9.3.1 --- Structure of the Neural Network --- p.174Chapter 9.3.2 --- Normalization of the Input and Output Patterns --- p.175Chapter 9.4 --- Experimental Results and Discussions --- p.178Chapter 9.4.1 --- Results for Lambertian Surface under One Rectangular Light --- p.178Chapter 9.4.2 --- Results for Lambertian Surface under Four Rectangular Light Sources --- p.180Chapter 9.4.3 --- Results for Hybrid Surface under One Rectangular Light Sources --- p.190Chapter 9.4.4 --- Discussions --- p.190Chapter 10 --- Summary and Conclusions --- p.191Chapter 10.1 --- Summary Results and Contributions --- p.192Chapter 10.2 --- Directions of Future Research --- p.199Bibliography --- p.20

T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects

We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.Comment: WACV 201

Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data

Reliable human pose estimation (HPE) is essential to many clinical applications, such as surgical workflow analysis, radiation safety monitoring and human-robot cooperation. Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room. Instead, we propose a novel approach based on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in real ORs. We extend the PS framework in two ways. First, we build robust and discriminative part detectors using both color and depth images. We also present a novel descriptor for depth images, called histogram of depth differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise constraints and a new method that makes exact inference tractable. Our approach is evaluated for pose estimation and clinician detection on a challenging RGB-D dataset recorded in a busy operating room during live surgeries. We conduct series of experiments to study the different part detectors in conjunction with the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D PS with RGB-D part detectors significantly improves the results in a visually challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg

Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications

In this paper we present a semi-automatic 2D-3D local registration pipeline capable of coloring 3D models obtained from 3D scanners by using uncalibrated images. The proposed pipeline exploits the Structure from Motion (SfM) technique in order to reconstruct a sparse representation of the 3D object and obtain the camera parameters from image feature matches. We then coarsely register the reconstructed 3D model to the scanned one through the Scale Iterative Closest Point (SICP) algorithm. SICP provides the global scale, rotation and translation parameters, using minimal manual user intervention. In the final processing stage, a local registration refinement algorithm optimizes the color projection of the aligned photos on the 3D object removing the blurring/ghosting artefacts introduced due to small inaccuracies during the registration. The proposed pipeline is capable of handling real world cases with a range of characteristics from objects with low level geometric features to complex ones

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset. Our code and dataset are available at https://rse-lab.cs.washington.edu/projects/posecnn/.Comment: Accepted to RSS 201