OpenMask3D: Open-Vocabulary 3D Instance Segmentation

We introduce the task of open-vocabulary 3D instance segmentation. Traditional approaches for 3D instance segmentation largely rely on existing 3D annotated datasets, which are restricted to a closed-set of object categories. This is an important limitation for real-life applications where one might need to perform tasks guided by novel, open-vocabulary queries related to objects from a wide variety. Recently, open-vocabulary 3D scene understanding methods have emerged to address this problem by learning queryable features per each point in the scene. While such a representation can be directly employed to perform semantic segmentation, existing methods have limitations in their ability to identify object instances. In this work, we address this limitation, and propose OpenMask3D, which is a zero-shot approach for open-vocabulary 3D instance segmentation. Guided by predicted class-agnostic 3D instance masks, our model aggregates per-mask features via multi-view fusion of CLIP-based image embeddings. We conduct experiments and ablation studies on the ScanNet200 dataset to evaluate the performance of OpenMask3D, and provide insights about the open-vocabulary 3D instance segmentation task. We show that our approach outperforms other open-vocabulary counterparts, particularly on the long-tail distribution. Furthermore, OpenMask3D goes beyond the limitations of close-vocabulary approaches, and enables the segmentation of object instances based on free-form queries describing object properties such as semantics, geometry, affordances, and material properties.Comment: project page: https://openmask3d.github.io

Entropy-driven liquid-liquid separation in supercooled water

Twenty years ago Poole et al. (Nature 360, 324, 1992) suggested that the anomalous properties of supercooled water may be caused by a critical point that terminates a line of liquid-liquid separation of lower-density and higher-density water. Here we present an explicit thermodynamic model based on this hypothesis, which describes all available experimental data for supercooled water with better quality and with fewer adjustable parameters than any other model suggested so far. Liquid water at low temperatures is viewed as an 'athermal solution' of two molecular structures with different entropies and densities. Alternatively to popular models for water, in which the liquid-liquid separation is driven by energy, the phase separation in the athermal two-state water is driven by entropy upon increasing the pressure, while the critical temperature is defined by the 'reaction' equilibrium constant. In particular, the model predicts the location of density maxima at the locus of a near-constant fraction (about 0.12) of the lower-density structure.Comment: 7 pages, 6 figures. Version 2 contains an additional supplement with tables for the mean-field equatio

Evidence of a landlocked reproducing population of the marine pejerrey Odontesthes argentinensis (Actinopterygii; Atherinopsidae)

In South America, the order Atheriniformes includes the monophyletic genus<em>Odontesthes</em> with 20 species that inhabit freshwater, estuarine and coastal environments. Pejerrey Odontesthes argentinensis is widely distributed in coastal and estuarineareas of the Atlantic Ocean and is known to foray into estuaries of river systems, particularly in conditions of elevated salinity. However, to our knowledge, a landlockedself-sustaining population has never been recorded. In this study, we examined the pejerrey population of Salada de Pedro Luro Lake (south-east of BuenosAires Province, Argentina) to clarify its taxonomic identity. An integrative taxonomic analysis based on traditional meristic, landmark-based morphometrics and genetictechniques suggests that the Salada de Pedro Luro pejerrey population represents a novel case of physiological and morphological adaptation of a marine pejerrey speciesto a landlocked environment and emphasises the environmental plasticity of this group of fishe

A case-control study to identify risk factors associated with avian influenza subtype H9N2 on commercial poultry farms in Pakistan

A 1:1 matched case-control study was conducted to identify risk factors for avian influenza subtype H9N2 infection on commercial poultry farms in 16 districts of Punjab, and 1 administrative unit of Pakistan. One hundred and thirty-three laboratory confirmed positive case farms were matched on the date of sample submission with 133 negative control farms. The association between a series of farm-level characteristics and the presence or absence of H9N2 was assessed by univariable analysis. Characteristics associated with H9N2 risk that passed the initial screening were included in a multivariable conditional logistic regression model. Manual and automated approaches were used, which produced similar models. Key risk factors from all approaches included selling of eggs/birds directly to live bird retail stalls, being near case/infected farms, a previous history of infectious bursal disease (IBD) on the farm and having cover on the water storage tanks. The findings of current study are in line with results of many other studies conducted in various countries to identify similar risk factors for AI subtype H9N2 infection. Enhancing protective measures and controlling risks identified in this study could reduce spread of AI subtype H9N2 and other AI viruses between poultry farms in Pakistan

Localizing and segmenting objects with 3D objectness

This paper presents a novel method to localize and segment objects on close-range table-top scenarios acquired with a depth sensor. The method is based on a novel objectness measure that evaluates how likely a 3D region in space (defined by an oriented 3D bounding box) could contain an object. Within a parametrized volume of interest placed above the table plane, a set of 3D bounding boxes is generated that exhaustively covers the parameter space. Efficiently evaluating \u2014 thanks to integral volumes and parallel computing\u2014 the 3D objectness at each sampled bounding box allows defining a set of regions in space with high probability of containing an object. Bounding boxes characterized by high objectness are then processed by means of a global optimization stage aimed at discarding inconsistent object hypotheses with respect to the scene. We evaluate the effectiveness of the method for the task of scene segmentation

Adaptive Low Resolution Pruning for Fast Full-Search Equivalente Pattern Matching

Several recent proposals have shown the feasibility of significantly speeding-up pattern matching by means of Full Search-equivalent techniques, i.e. without approximating the outcome of the search with respect to a brute force investigation. These techniques are generally heavily based on efficient incremental calculation schemes aimed at avoiding unnecessary computations. In a very recent and extensive experimental evaluation, Low Resolution Pruning turned out to be in most cases the best performing approach. In this paper we propose a computational analysis of several incremental techniques specifically designed to enhance the efficiency of LRP. In addition, we propose a novel LRP algorithm aimed at minimizing the theoretical number of operations by adaptively exploiting different incremental approaches. We demonstrate the effectiveness of our proposal by means of experimental evaluation on a large dataset

Les mutations virales et leur impact sur la vaccination contre la bursite infectieuse (maladie de Gumboro)

International audienceInfectious bursal disease (also known as Gumboro disease) is an immunosuppressive viral disease specific to chickens. In spite of all the information amassed on the antigenic and immunological characteristics of the virus, the disease has not yet been brought fully under control. It is still prevalent in properly vaccinated flocks carrying specific antibodies at levels normally high enough to prevent the disease. Common causes apart, failure of vaccination against infectious bursal disease is associated mainly with early vaccination in flocks of unknown immune status and with the evolution of viruses circulating in the field, leading to antigenic drift and a sharp rise in pathogenicity. Various highly sensitive molecular techniques have clarified the viral determinants of antigenicity and pathogenicity of the infectious bursal disease virus. However, these markers are not universally recognised and tend to be considered as evolutionary markers. Antigenic variants of the infectious bursal disease virus possess modified neutralising epitopes that allow them to evade the action of maternally-derived or vaccine-induced antibodies. Autogenous or multivalent vaccines are required to control antigenic variants in areas where classical and variant virus strains coexist. Pathotypic variants (very virulent viruses) remain antigenically related to classical viruses. The difficulty in controlling pathotypic variants is linked to the difficulty of eliciting an early immune response, because of the risk of the vaccine virus being neutralised by maternal antibodies. Mathematical calculation of the optimal vaccination time and the use of vaccines resistant to maternally-derived antibodies have improved the control of very virulent viruses.La bursite infectieuse (maladie de Gumboro) est une pathologie virale immunodépressive spécifique du poulet. En dépit des informations accumulées sur les caractères antigéniques et immunologiques du virus, la maladie reste imparfaitement contrôlée. Elle sévit aujourd’hui dans des cheptels correctement vaccinés et porteurs d’anticorps spécifiques à des niveaux habituellement suffisants pour prévenir la maladie. Au-delà des causes triviales, les échecs de la vaccination contre la maladie de Gumboro sont essentiellement liés aux vaccinations précoces de cheptels au statut immunitaire inconnu et à l’évolution des virus qui circulent sur le terrain, se traduisant par une dérive antigénique et une hausse sensible de la pathogénicité. Diverses techniques moléculaires hautement sensibles ont permis d’identifier les déterminants viraux d’antigénicité et de pathogénicité du virus. Ces marqueurs ne sont cependant pas unanimement reconnus et sont pour la plupart considérés comme des marqueurs évolutionnaires. Les virus variants antigéniques possèdent des épitopes neutralisants modifiés qui leur permettent de se soustraire à l’action des anticorps résiduels ou vaccinaux. Leur contrôle passe par l’utilisation d’autovaccins ou de vaccins multivalents dans les régions où coexistent virus classiques et variants. Les variants pathotypiques (virus hypervirulents) restent antigéniquement apparentés aux virus classiques. La difficulté de contrôler ce type de variant est liée à celle d’obtenir une réponse immune précoce, en raison du risque de neutralisation du virus vaccinal par les anticorps d’origine maternelle. Le calcul mathématique de la date optimale de vaccination et l’utilisation de vaccins insensibles aux anticorps résiduels ont permis un meilleur contrôle des virus hypervirulents

Performance Evaluation of Full Search Equivalent Pattern Matching Algorithms

Pattern matching is widely used in signal processing, computer vision, and image and video processing. Full search equivalent algorithms accelerate the pattern matching process and, in the meantime, yield exactly the same result as the full search. This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching. Our intention is that the data sets and tests used in our evaluation will be a benchmark for testing future pattern matching algorithms, and that the analysis concerning state-of-the-art algorithms could inspire new fast algorithms. We also propose extensions of the evaluated algorithms and show that they outperform the original formulations

OpenMask3D: Open-Vocabulary 3D Instance Segmentation

We introduce the task of open-vocabulary 3D instance segmentation. Current approaches for 3D instance segmentation can typically only recognize object categories from a pre-defined closed set of classes that are annotated in the training datasets. This results in important limitations for real-world applications where one might need to perform tasks guided by novel, open-vocabulary queries related to a wide variety of objects. Recently, open-vocabulary 3D scene understanding methods have emerged to address this problem by learning queryable features for each point in the scene. While such a representation can be directly employed to perform semantic segmentation, existing methods cannot separate multiple object instances. In this work, we address this limitation, and propose OpenMask3D, which is a zero-shot approach for open-vocabulary 3D instance segmentation. Guided by predicted class-agnostic 3D instance masks, our model aggregates per-mask features via multi-view fusion of CLIP-based image embeddings. Experiments and ablation studies on ScanNet200 and Replica show that OpenMask3D outperforms other open-vocabulary methods, especially on the long-tail distribution. Qualitative experiments further showcase OpenMask3D’s ability to segment object properties based on free-form queries describing geometry, affordances, and materials