Going Further with Point Pair Features

Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016; https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5

Unfreezing of molecular motions in protein-polymer conjugates: a calorimetric study

Protein-polymer conjugates are a promising class of biohybrids. In this work, the dynamics of a set of biodegradable conjugates myoglobin-poly(ethyl ethylene phosphate) (My-PEEP) with variations in the number of attached polymers and their molar mass in the dry-state, have been investigated to understand the role of polymer on protein dynamics. We performed Differential Scanning Calorimetry measurements between 190 and 300 K, observing the large-scale dynamics arising from reorganization of conformational states, i.e. within the 100 s timescale. The application of an annealing time during the cooling scans was used to investigate the non-equilibrium glassy-state of the samples, observing the relaxation enthalpy at different annealing temperatures. This procedure permitted to extensively describe the transition broadness and the system relaxation kinetics in the glassy state. The samples show an experimental behaviour different from the theoretical predictions, suggesting the establishment of interactions among the protein and the polymer chains. The different behaviour of the conjugates and the physical mixture (composed of the protein and the polymer physically mixed) highlighted the importance of the covalent bond in defining the system dynamics

{SHIFT}: {A} Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift

Shrec'16 Track: Retrieval of Human Subjects from Depth Sensor Data

International audienceIn this paper we report the results of the SHREC 2016 contest on "Retrieval of human subjects from depth sensor data". The proposed task was created in order to verify the possibility of retrieving models of query human subjects from single shots of depth sensors, using shape information only. Depth acquisition of different subjects were realized under different illumination conditions, using different clothes and in three different poses. The resulting point clouds of the partial body shape acquisitions were segmented and coupled with the skeleton provided by the OpenNI software and provided to the participants together with derived triangulated meshes. No color information was provided. Retrieval scores of the different methods proposed were estimated on the submitted dissimilarity matrices and the influence of the different acquisition conditions on the algorithms were also analyzed. Results obtained by the participants and by the baseline methods demonstrated that the proposed task is, as expected, quite difficult, especially due the partiality of the shape information and the poor accuracy of the estimated skeleton, but give useful insights on potential strategies that can be applied in similar retrieval procedures and derived practical applications. Categories and Subject Descriptors (according to ACM CCS): I.4.8 [IMAGE PROCESSING AND COMPUTER VISION]: Scene Analysis—Shap

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

Boosting Object Recognition in Point Clouds by Saliency Detection

Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications. Local descriptors are an amenable choice whenever the 6 DoF pose of recognized objects should also be estimated. However, the pipeline for this kind of descriptors is highly time-consuming. In this work, we propose an update to the traditional pipeline, by adding a preliminary filtering stage referred to as saliency boost. We perform tests on a standard object recognition benchmark by considering four keypoint detectors and four local descriptors, in order to compare time and recognition performance between the traditional pipeline and the boosted one. Results on time show that the boosted pipeline could turn out up to 5 times faster, with the recognition rate improving in most of the cases and exhibiting only a slight decrease in the others. These results suggest that the boosted pipeline can speed-up processing time substantially with limited impacts or even benefits in recognition accuracy.Comment: International Conference on Image Analysis and Processing (ICIAP) 201

トクシュウ トクシマケン ノ キュウキュウ イリョウ ト チイキ イリョウ : ゲンジョウ ト テンボウ : カントウゲン

We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings

Giant hydronephrosis mimicking progressive malignancy

BACKGROUND: Cases of giant hydronephroses are rare and usually contain no more than 1–2 litres of fluid in the collecting system. We report a remarkable case of giant hydronephrosis mimicking a progressive malignant abdominal tumour. CASE PRESENTATION: A 78-year-old cachectic woman presented with an enormous abdominal tumour, which, according to the patient, had slowly increased in diameter. Medical history was unremarkable except for a hysterectomy >30 years before. A CT scan revealed a giant cystic tumour filling almost the entire abdominal cavity. It was analysed by two independent radiologists who suspected a tumour originating from the right kidney and additionally a cystic ovarian neoplasm. Subsequently, a diagnostic and therapeutic laparotomy was performed: the tumour presented as a cystic, 35 × 30 × 25 cm expansive structure adhesive to adjacent organs without definite signs of invasive growth. The right renal hilar vessels could finally be identified at its basis. After extirpation another tumourous structure emerged in the pelvis originating from the genital organs and was also resected. The histopathological examination revealed a >15 kg hydronephrotic right kidney, lacking hardly any residual renal cortex parenchyma. The second specimen was identified as an ovary with regressive changes and a large partially calcified cyst. There was no evidence of malignant growth. CONCLUSION: Although both clinical symptoms and the enormous size of the tumour indicated malignant growth, it turned out to be a giant hydronephrosis. Presumably, a chronic obstruction of the distal ureter had caused this extraordinary hydronephrosis. As demonstrated in our case, an accurate diagnosis of giant hydronephrosis remains challenging due to the atrophy of the renal parenchyma associated with chronic obstruction. Therefore, any abdominal cystic mass even in the absence of other evident pathologies should include the differential diagnosis of a possible hydronephrosis. Diagnostic accuracy might be increased by a combination of endourological techniques such as retrograde pyelography and modern imaging modalities

Tuning of Adaptive Weight Depth Map Generation Algorithms Exploratory Data Analysis and Design of Computer Experiments (DOCE)

In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on un planned experiments -- Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards -- This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative inf uence of the parameters and to fine-tune them based on the number of bad pixels -- The implemented methodology improves the performance of adaptive weight based dense depth map algorithms -- As a result, the algorithm improves from 16.78% to 14.48% bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78% to 13

Mutant p53 sustains serine-glycine synthesis and essential amino acids intake promoting breast cancer growth

: Reprogramming of amino acid metabolism, sustained by oncogenic signaling, is crucial for cancer cell survival under nutrient limitation. Here we discovered that missense mutant p53 oncoproteins stimulate de novo serine/glycine synthesis and essential amino acids intake, promoting breast cancer growth. Mechanistically, mutant p53, unlike the wild-type counterpart, induces the expression of serine-synthesis-pathway enzymes and L-type amino acid transporter 1 (LAT1)/CD98 heavy chain heterodimer. This effect is exacerbated by amino acid shortage, representing a mutant p53-dependent metabolic adaptive response. When cells suffer amino acids scarcity, mutant p53 protein is stabilized and induces metabolic alterations and an amino acid transcriptional program that sustain cancer cell proliferation. In patient-derived tumor organoids, pharmacological targeting of either serine-synthesis-pathway and LAT1-mediated transport synergizes with amino acid shortage in blunting mutant p53-dependent growth. These findings reveal vulnerabilities potentially exploitable for tackling breast tumors bearing missense TP53 mutations