Results of minimally invasive quick definitive fixation of unstable bony pelvic disruption by combined retrosacral transiliac rods and anterior external fixator during a critical national period

Unstable pelvic ring fractures are challenging injuries regarding their reduction and stabilization. The presented study evaluates the results of a minimally invasive and quick one-stage stabilization of sacral fractures combined with bilateral pubic rami fractures during a period of national limited resources and decreased general security aiming at reduction of the duration of hospital stay and overall costs. Sixteen patients with unilateral sacral fractures and bilateral pubic rami fractures without lumbosacral dissociation were fixed by two retrosacral threaded transiliac rods and an anterior external fixator. Results were assessed with Majeed score and Matta-Tornetta radiologic criteria for post-operative reduction. The follow up period averaged 23 months. There were 9 cases excellent, 4 cases good and 3 cases fair. The duration of surgery and the number of intra-operative X-ray images were recorded. The presented technique is simple, reproducible and quick for one-stage fixation of the unstable pelvic bony disruption. It reduces the operative time, radiation exposure, duration of hospital stays and cost of care during a critical national period with limited resources.

SATR: Zero-Shot Semantic Segmentation of 3D Shapes

We explore the task of zero-shot semantic segmentation of 3D shapes by using large-scale off-the-shelf 2D image recognition models. Surprisingly, we find that modern zero-shot 2D object detectors are better suited for this task than contemporary text/image similarity predictors or even zero-shot 2D segmentation networks. Our key finding is that it is possible to extract accurate 3D segmentation maps from multi-view bounding box predictions by using the topological properties of the underlying surface. For this, we develop the Segmentation Assignment with Topological Reweighting (SATR) algorithm and evaluate it on two challenging benchmarks: FAUST and ShapeNetPart. On these datasets, SATR achieves state-of-the-art performance and outperforms prior work by at least 22\% on average in terms of mIoU. Our source code and data will be publicly released. Project webpage: https://samir55.github.io/SATR/Comment: Project webpage: https://samir55.github.io/SATR

ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes

The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data. In this paper, we curate a large-scale and complementary dataset extending both the aforementioned ones by associating all objects mentioned in a referential sentence to their underlying instances inside a 3D scene. Specifically, our Scan Entities in 3D (ScanEnts3D) dataset provides explicit correspondences between 369k objects across 84k natural referential sentences, covering 705 real-world scenes. Crucially, we show that by incorporating intuitive losses that enable learning from this novel dataset, we can significantly improve the performance of several recently introduced neural listening architectures, including improving the SoTA in both the Nr3D and ScanRefer benchmarks by 4.3% and 5.0%, respectively. Moreover, we experiment with competitive baselines and recent methods for the task of language generation and show that, as with neural listeners, 3D neural speakers can also noticeably benefit by training with ScanEnts3D, including improving the SoTA by 13.2 CIDEr points on the Nr3D benchmark. Overall, our carefully conducted experimental studies strongly support the conclusion that, by learning on ScanEnts3D, commonly used visio-linguistic 3D architectures can become more efficient and interpretable in their generalization without needing to provide these newly collected annotations at test time. The project's webpage is https://scanents3d.github.io/ .Comment: The project's webpage is https://scanents3d.github.io

3DCoMPaT++^{++}: An improved Large-scale 3D Vision Dataset for Compositional Recognition

In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-instance level, alongside matching RGB point clouds, 3D textured meshes, depth maps, and segmentation masks. 3DCoMPaT$^{++}$ covers 41 shape categories, 275 fine-grained part categories, and 293 fine-grained material classes that can be compositionally applied to parts of 3D objects. We render a subset of one million stylized shapes from four equally spaced views as well as four randomized views, leading to a total of 160 million renderings. Parts are segmented at the instance level, with coarse-grained and fine-grained semantic levels. We introduce a new task, called Grounded CoMPaT Recognition (GCR), to collectively recognize and ground compositions of materials on parts of 3D objects. Additionally, we report the outcomes of a data challenge organized at CVPR2023, showcasing the winning method's utilization of a modified PointNet$^{++}$ model trained on 6D inputs, and exploring alternative techniques for GCR enhancement. We hope our work will help ease future research on compositional 3D Vision.Comment: https://3dcompat-dataset.org/v2

Review: Current trends in coral transplantation – an approach to preserve biodiversity

Ammar MSA,El-Gammal F, Nassar M, Belal A, Farag W, El-Mesiry G, El-Haddad K, Orabi A, Abdelreheem A, Shaaban A. 2013. Review: Current trends in coral transplantation – an approach to preserve biodiversity. Biodiversitas 14: 43-53. The increasing rates of coral mortality associated with the rise in stress factors and the lack of adequate recovery worldwide have urged recent calls for actions by the scientific, conservation, and reef management communities. This work reviews the current trends in coral transplantation. Transplantation of coral colonies or fragments, whether from aqua-, mariculture or harvesting from a healthy colony, has been the most frequently recommended action for increasing coral abundance on damaged or degraded reefs and for conserving listed or “at-risk” species. Phytoplanktons are important for providing transplanted corals with complex organic compounds through photosynthesis. Artificial surfaces like concrete blocks, wrecks or other purpose-designed structures can be introduced for larval settlement. New surfaces can also be created through electrolysis. Molecular biological tools can be used to select sites for rehabilitation by asexual recruits. Surface chemistry and possible inputs of toxic leachate from artificial substrates are considered as important factors affecting natural recruitment. Transplants should be carefully maintained , revisited and reattached at least weekly in the first month and at least fortnightly in the next three months. Studies on survivorship and the reproductive ability of transplanted coral fragments are important for coral reef restoration. A coral nursery may be considered as a pool for local species that supplies reef-managers with unlimited coral colonies for sustainable management. Transplanting corals for making artificial reefs can be useful for increasing biodiversity, providing tourist diving, fishing and surfing; creating new artisanal and commercial fishing opportunities, colonizing structures by fishes and invertebrates), saving large corals during the construction of a Liquified Natural Gas Plant

SATR: Zero-Shot Semantic Segmentation of 3D Shapes

International audienceWe explore the task of zero-shot semantic segmentation of 3D shapes by using large-scale off-the-shelf 2D image recognition models. Surprisingly, we find that modern zero-shot 2D object detectors are better suited for this task than contemporary text/image similarity predictors or even zero-shot 2D segmentation networks. Our key finding is that it is possible to extract accurate 3D segmentation maps from multi-view bounding box predictions by using the topological properties of the underlying surface. For this, we develop the Segmentation Assignment with Topological Reweighting (SATR) algorithm and evaluate it on ShapeNetPart and our proposed FAUST benchmarks. SATR achieves state-of-the-art performance and outperforms a baseline algorithm by 1.3% and 4% average mIoU on the FAUST coarse and fine-grained benchmarks, respectively, and by 5.2% average mIoU on the ShapeNetPart benchmark. Our source code and data will be publicly released. Project webpage: https://samir55.github.io/SATR/

Zero-Shot 3D Shape Correspondence

International audienceWe propose a novel zero-shot approach to computing correspondences between 3D shapes.Existing approaches mainly focus on isometric and near-isometric shape pairs (\textit{e}.\textit{g}., human vs. human), but less attention has been given to strongly \emph{non-isometric} and \emph{inter-class} shape matching (\textit{e}.\textit{g}., human vs. cow).To this end, we introduce a fully automatic method that exploits the exceptional reasoning capabilities of recent foundation models in language and vision to tackle difficult shape correspondence problems.Our approach comprises multiple stages.First, we classify the 3D shapes in a zero-shot manner by feeding rendered shape views to a language-vision model (\textit{e}.\textit{g}., BLIP2) to generate a list of class proposals per shape.These proposals are unified into a single class per shape by employing the reasoning capabilities of ChatGPT.Second, we attempt to segment the two shapes in a zero-shot manner, but in contrast to the co-segmentation problem, we do not require a mutual set of semantic regions.Instead, we propose to exploit the in-context learning capabilities of ChatGPT to generate two different sets of \emph{semantic regions} for each shape and a \emph{semantic mapping} between them. This enables our approach to match strongly non-isometric shapes with significant differences in geometric structure.Finally, we employ the generated semantic mapping to produce coarse correspondences that can further be refined by the functional maps framework to produce dense point-to-point maps.Our approach\footnote{Project webpage: \projecthref}, despite its simplicity, produces highly plausible results in a zero-shot manner, especially between \emph{strongly non-isometric} shapes

SATR: Zero-Shot Semantic Segmentation of 3D Shapes

International audienceWe explore the task of zero-shot semantic segmentation of 3D shapes by using large-scale off-the-shelf 2D image recognition models. Surprisingly, we find that modern zero-shot 2D object detectors are better suited for this task than contemporary text/image similarity predictors or even zero-shot 2D segmentation networks. Our key finding is that it is possible to extract accurate 3D segmentation maps from multi-view bounding box predictions by using the topological properties of the underlying surface. For this, we develop the Segmentation Assignment with Topological Reweighting (SATR) algorithm and evaluate it on ShapeNetPart and our proposed FAUST benchmarks. SATR achieves state-of-the-art performance and outperforms a baseline algorithm by 1.3% and 4% average mIoU on the FAUST coarse and fine-grained benchmarks, respectively, and by 5.2% average mIoU on the ShapeNetPart benchmark. Our source code and data will be publicly released. Project webpage: https://samir55.github.io/SATR/