End2End Multi-View Feature Matching with Differentiable Pose Optimization

Erroneous feature matches have severe impact on subsequent camera pose estimation and often require additional, time-costly measures, like RANSAC, for outlier rejection. Our method tackles this challenge by addressing feature matching and pose optimization jointly. To this end, we propose a graph attention network to predict image correspondences along with confidence weights. The resulting matches serve as weighted constraints in a differentiable pose estimation. Training feature matching with gradients from pose optimization naturally learns to down-weight outliers and boosts pose estimation on image pairs compared to SuperGlue by 6.7% on ScanNet. At the same time, it reduces the pose estimation time by over 50% and renders RANSAC iterations unnecessary. Moreover, we integrate information from multiple views by spanning the graph across multiple frames to predict the matches all at once. Multi-view matching combined with end-to-end training improves the pose estimation metrics on Matterport3D by 18.5% compared to SuperGlue.Comment: ICCV 2023, project page: https://barbararoessle.github.io/e2e_multi_view_matching , video: https://youtu.be/uuLb6GfM9C

GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields

Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitigate these imperfections from various sources with a joint solution: we take advantage of the ability of generative adversarial networks (GANs) to produce realistic images and use them to enhance realism in 3D scene reconstruction with NeRFs. To this end, we learn the patch distribution of a scene using an adversarial discriminator, which provides feedback to the radiance field reconstruction, thus improving realism in a 3D-consistent fashion. Thereby, rendering artifacts are repaired directly in the underlying 3D representation by imposing multi-view path rendering constraints. In addition, we condition a generator with multi-resolution NeRF renderings which is adversarially trained to further improve rendering quality. We demonstrate that our approach significantly improves rendering quality, e.g., nearly halving LPIPS scores compared to Nerfacto while at the same time improving PSNR by 1.4dB on the advanced indoor scenes of Tanks and Temples.Comment: Video: https://youtu.be/EUWW8nUxpl

Harold T. Shapiro, Bryant Gumbel, Today Show (image)

[This issue was printed with the incorrect date]http://deepblue.lib.umich.edu/bitstream/2027.42/61111/1/1703.pd

The angiogenic inhibitor long pentraxin PTX3 forms an asymmetric octamer with two binding sites for FGF2

The inflammation-associated long pentraxin PTX3 plays key roles in innate immunity, female fertility, and vascular biology (e.g. it inhibits FGF2 (fibroblast growth factor 2)-mediated angiogenesis). PTX3 is composed of multiple protomers, each composed of distinct N- and C-terminal domains; however, it is not known how these are organized or contribute to its functional properties. Here, biophysical analyses reveal that PTX3 is composed of eight identical protomers, associated through disulfide bonds, forming an elongated and asymmetric, molecule with two differently sized domains interconnected by a stalk. The N-terminal region of the protomer provides the main structural determinant underlying this quaternary organization, supporting formation of a disulfide-linked tetramer and a dimer of dimers (a non-covalent tetramer), giving rise to the asymmetry of the molecule. Furthermore, the PTX3 octamer is shown to contain two FGF2 binding sites, where it is the tetramers that act as the functional units in ligand recognition. Thus, these studies provide a unifying model of the PTX3 oligomer, explaining both its quaternary organization and how this is required for its antiangiogenic function