An Eocene orthocone from Antarctica shows convergent evolution of internally shelled cephalopods

Background The Subclass Coleoidea (Class Cephalopoda) accommodates the diverse present-day internally shelled cephalopod mollusks (Spirula, Sepia and octopuses, squids, Vampyroteuthis) and also extinct internally shelled cephalopods. Recent Spirula represents a unique coleoid retaining shell structures, a narrow marginal siphuncle and globular protoconch that signify the ancestry of the subclass Coleoidea from the Paleozoic subclass Bactritoidea. This hypothesis has been recently supported by newly recorded diverse bactritoid-like coleoids from the Carboniferous of the USA, but prior to this study no fossil cephalopod indicative of an endochochleate branch with an origin independent from subclass Bactritoidea has been reported. Methodology/Principal findings Two orthoconic conchs were recovered from the Early Eocene of Seymour Island at the tip of the Antarctic Peninsula, Antarctica. They have loosely mineralized organic-rich chitincompatible microlaminated shell walls and broadly expanded central siphuncles. The morphological, ultrustructural and chemical data were determined and characterized through comparisons with extant and extinct taxa using Scanning Electron Microscopy/Energy Dispersive Spectrometry (SEM/EDS). Conclusions/Significance Our study presents the first evidence for an evolutionary lineage of internally shelled cephalopods with independent origin from Bactritoidea/Coleoidea, indicating convergent evolution with the subclass Coleoidea. A new subclass Paracoleoidea Doguzhaeva n. subcl. is established for accommodation of orthoconic cephalopods with the internal shell associated with a broadly expanded central siphuncle. Antarcticerida Doguzhaeva n. ord., Antarcticeratidae Doguzhaeva n. fam., Antarcticeras nordenskjoeldi Doguzhaeva n. gen., n. sp. are described within the subclass Paracoleoidea. The analysis of organic-rich shell preservation of A. nordenskjoeldi by use of SEM/EDS techniques revealed fossilization of hyposeptal cameral soft tissues. This suggests that a depositional environment favoring soft-tissue preservation was the factor enabling conservation of the weakly mineralized shell of A. nordenskjoeldi.Facultad de Ciencias Naturales y Muse

The Weng’an Biota (Doushantuo Formation):an Ediacaran window on soft bodied and multicellular microorganisms

The Weng’an Biota is a fossil Konservat-Lagerstätte in South China that is approximately 570-600 million years old and provides an unparalleled snapshot of marine life during the interval in which molecular clocks estimate that animal clades were diversifying. It yields specimens that are three-dimensionally preserved in calcium phosphate with cellular and sometimes subcellular fidelity. The biota includes candidates for the oldest animals in the fossil record, including embryonic, larval and adult forms. We argue that, while the Weng’an Biota includes forms that could be animals, none can currently be assigned to this group with confidence. Nonetheless, the biota offers a rare and valuable window on the evolution of multicellular and soft-bodied organisms in the prelude to the Cambrian radiation

Pose Estimation from RGB Images of Highly Symmetric Objects using a Novel Multi-Pose Loss and Differential Rendering

We propose a novel multi-pose loss function to train a neural network for 6D pose estimation, using synthetic data and evaluating it on real images. Our loss is inspired by the VSD (Visible Surface Discrepancy) metric and relies on a differentiable renderer and CAD models. This novel multi-pose approach produces multiple weighted pose estimates to avoid getting stuck in local minima. Our method resolves pose ambiguities without using predefined symmetries. It is trained only on synthetic data. We test on real-world RGB images from the T-LESS dataset, containing highly symmetric objects common in industrial settings. We show that our solution can be used to replace the codebook in a state-of-the-art approach. So far, the codebook approach has had the shortest inference time in the field. Our approach reduces inference time further while a) avoiding discretization, b) requiring a much smaller memory footprint and c) improving pose recall

A Shared Pose Regression Network for Pose Estimation of Objects from RGB Images

In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance