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

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

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

Gaussians-to-Life: {T}ext-Driven Animation of {3D Gaussian} Splatting Scenes

State-of-the-art novel view synthesis methods achieve impressive results formulti-view captures of static 3D scenes. However, the reconstructed scenesstill lack "liveliness," a key component for creating engaging 3D experiences.Recently, novel video diffusion models generate realistic videos with complexmotion and enable animations of 2D images, however they cannot naively be usedto animate 3D scenes as they lack multi-view consistency. To breathe life intothe static world, we propose Gaussians2Life, a method for animating parts ofhigh-quality 3D scenes in a Gaussian Splatting representation. Our key idea isto leverage powerful video diffusion models as the generative component of ourmodel and to combine these with a robust technique to lift 2D videos intomeaningful 3D motion. We find that, in contrast to prior work, this enablesrealistic animations of complex, pre-existing 3D scenes and further enables theanimation of a large variety of object classes, while related work is mostlyfocused on prior-based character animation, or single 3D objects. Our modelenables the creation of consistent, immersive 3D experiences for arbitraryscenes.<br

Geometry of the matching distance for 2D filtering functions

In this paper we exploit the concept of extended Pareto grid to study the geometric properties of the matching distance for R^2-valued regular functions defined on a closed Riemannian manifold. In particular, we prove that in this case the matching distance is realised either at special values or at values corresponding to vertical, horizontal or slope 1 lines

A Discrete-Element-Based Approach to Generate Random Parameters for Soil Fatigue Models (article)

This is the final version. Available on open access from MDPI via the DOI in this recordThe research data supporting this publication is available in ORE at https://doi.org/10.24378/exe.5786The structural reliability of bottom-fixed offshore wind turbines is generally influenced by the dispersion of and variability in soil properties, which affect their ultimate capacity, serviceability, and both the short- and long-term fatigue. During an earthquake, the soil–pile system is subjected to intense cyclic loads that can lead to stiffness and strength degradation, typically captured through cyclic soil models. Calibration of soil parameter variability is fundamental for reliable structural assessments of wind turbine integrity. In this study, a method to generate randomness of the parameters affecting cyclic soil degradation models is proposed. Fatigue parameters are quantified through random cyclic undrained triaxial tests conducted using the Discrete Element Method. Deterministic simulations are first performed based on experimental results from the Liquefaction Experiments and Analysis Project for validation. Subsequently, variability in the initial particle size distribution functions is introduced to generate random soil samples, and triaxial simulations are repeated to quantify the dispersion of soil fatigue parameters. The proposed procedure is then applied through Monte Carlo simulations on the IEA 15-MW reference wind turbine, which is subjected to both short- and long-duration earthquakes. The results demonstrate the significant impact of soil degradation on the bending moment envelope, as well as the effect of soil uncertainty on tower fatigue, assessed using the damage equivalent load approach.Engineering and Physical Sciences Research Council (EPSRC

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

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

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional training and architectural modifications. Meanwhile, large-scale generative diffusion models encode rich semantic information, making them attractive as general-purpose feature extractors. In this work, we introduce a new method that directly exploits features, attention scores, from diffusion transformers for downstream tasks, requiring neither architectural modifications nor additional training. To systematically evaluate these features, we extend benchmarks with vision-language grounding tasks spanning both images and videos. Our key insight is that stop words act as attention magnets: they accumulate surplus attention and can be filtered to reduce noise. Moreover, we identify global attention sinks (GAS) emerging in deeper layers and show that they can be safely suppressed or redirected onto auxiliary tokens, leading to sharper and more accurate grounding maps. We further propose an attention redistribution strategy, where appended stop words partition background activations into smaller clusters, yielding sharper and more localized heatmaps. Building on these findings, we develop RefAM, a simple training-free grounding framework that combines cross-attention maps, GAS handling, and redistribution. Across zero-shot referring image and video segmentation benchmarks, our approach consistently outperforms prior methods, establishing a new state of the art without fine-tuning or additional components