Learning and Understanding in 3D Point Clouds (Video1)Video Length:
We present a revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching. In particular, we propose to couple the object detection with a coarse-to-fine segmentation, where each segment is subject to disjoint pose estimation. During matching, we apply a weighted Hough voting and an interpolated recovery of pose parameters. Finally, all the generated hypotheses are tested via an occlusion-aware ranking and sorted. We argue that such a combined pipeline simultaneously boosts the detection rate and reduces the complexity, while improving the accuracy of the resulting pose.
6 July 2018, MI 03.13.010 Invited Talk by Prof. William (Sandy) Wells Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. [...]
8 October 2018, 01.06.020 Signed Distance Fields for Rigid and Deformable 3D Reconstruction Capturing three-dimensional environments is a key task in the growing fields of virtual and augmented reality. This thesis addresses the task of 3D reconstruction of both static and dynamic objects and scenes scanned with a single hand-held RGB-D camera, without any markers or prior knowledge. Reconstructing rigid environments requires estimating the six degrees-of-freedom camera pose at every time instance, and subsequently fusing the acquired data into a geometrically consistent computer model. The task of reconstructing deformable objects is more challenging, as additionally the non-rigid motion that occurred in every frame has to be determined and factored out. We propose to tackle both the rigid and deformable reconstruction problems via implicit-to-implicit alignment of SDF pairs without correspondence search. In the static case, we obtain more accurate pose estimates with a framework that permits straightforward incorporation of various additional constraints, such as surface colour and orientation. We start with the reconstruction of small- to medium-scale household objects and demonstrate how to extend the approach to larger spaces such as rooms. To this end, we develop a limited-extent volume strategy that restricts registration to the most geometrically distinctive regions of a scene, leading to significantly improved rotational motion estimation. Finally, we adapt our approach to dynamic scenes by modifying our implicit-to-implicit approach so that new data is incremented appropriately. For this purpose we evolve an initial SDF to a target SDF by imposing rigidity constraints that require the underlying deformation field to be approximately Killing, i.e. volume-preserving and generating locally isometric motions. Alternatively, we employ gradient flow in the smooth Sobolev space, which favours global deformations over finer-scale details. These strategies also circumvent explicit correspondence search and thus avoid the repeated conversion between SDF and mesh representations that other techniques entail. Nevertheless, we ensure that correspondence information can be recovered by proposing two strategies based on Laplacian eigenfunctions, which are known to encode natural deformation patterns. Thanks to the used SDF representation, our non-rigid reconstruction approach is able to handle topological changes and fast motion, which are major obstacles for existing approaches.
10 September 2018, IFL Invited Talk by Dr. Hayato Itoh In colon cancer screening, polyp size estimation using only colonoscopy images or videos is difficult even for expert physicians although the size information of polyps is important for diagnosis. To construct a fully automated computer-aided diagnosis (CAD) pipeline, a robust and precise polyp size estimation method is highly desired. However, the size estimation problem of a three-dimensional object from a single two-dimensional image is ill-posed due to the lack of three-dimensional spatial information. To circumvent this challenge, we formulate a relaxed form of size estimation as a binary-size classification problem and solve it by a new deep neural network architecture. This relaxed form of size estimation is defined as a two-category classification: under and over a certain polyp dimension criterion that would provoke different clinical treatments (resecting the polyp or not). Our proposed deep learning architecture estimates the depth map from an input colonoscopic RGB image using unsupervised deep learning, and integrates RGB with the computed depth information to produce a four-channel RGB-D imagery data, that is subsequently encoded as a pipeline to extract deep RGB-D image features and facilitate the size classification into two categories: under and over 10mm polyps. We collect a large dataset of colonoscopic videos of totally over 16 hours is constructed for the evaluation of our proposed method. Using this dataset, we evaluate the accuracies of both polyp detection and binary polyp-size estimation since detection is a prerequisite step of a fully automated CAD system. The experimental results show that our proposed method achieves 79.2% accuracy for binary polyp-size classification. We also combine the extraction features by our deep learning architecture and classification of short video clips using a long short-term memory (LSTM) network. Polyp detection (if the video clip contains a polyp or not) shows 88.8% sensitivity when employing the spatio-temporal image feature extraction and classification.
7 September 2018, 01.07.014 Learning Context For Semantic Segmentation And Applications Nowadays, cameras are an integral part of many devices and systems - from mobile phones to autonomous vehicles and from medical robots to surveillance cameras. While an active field of research, the task of understanding a camera image still poses significant challenges and is not solved in general. A key in interpreting camera images correctly is not to focus on individual image areas or objects, but to use context from the whole image in order to resolve ambiguities - something that we humans are very good at...
5 July 2018, MI 01.11.018 PhD Defense by Sailesh Conjeti The task of similarity search refers to fetching an item that is closest to the query item from a reference database under the notion of some distance measure. In critical applications of large-scale search and pattern matching, exhaustive comparison is often not possible due to prohibitive computational complexity and memory overheads. Towards mitigating this, hashing has been adopted as a popular approach for performing computationally efficient approximate nearest neighbor search. [...]