최근 애플 연구진이 발표한 LiTo 모델은 단 한 장의 사진만으로도 3D 객체의 기하학적 형태와 조명 효과까지 완벽하게 재구성합니다. 지금까지 3D 모델링을 위해 여러 각도에서 찍은 수많은 데이터가 필요했던 과거의 방식과는 차원이 다른 변화입니다. 사진 한 장 속에 담긴 정보만으로

A preliminary #3DReconstruction video is an early draft of a scene reconstruction made from whatever evidence is available so far like measurements, photos, video, and sometimes 3D scans. It turns that information into a computer model and can animate where people, vehicles, and camera views may have been. “Preliminary” means it can change as more evidence comes in and as experts verify what is accurate. In a #Trial, this can be powerful because it helps a jury see distances, timing, lines of sight, and whether a person could realistically perceive a threat. It can also help lawyers test claims and spot missing facts. But it only helps if it is grounded in real evidence and explained by a qualified expert, because the other side can challenge the assumptions and a judge can keep it out if it is misleading or unfairly prejudicial. #ReneeNicoleGood #ICE #minnesota Credit: @index.ngo @misanharriman

Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes

An explicit numerical leveling of non-uniformly distributed sets of 2D projections with the program VUE improves the 3D reconstructions and illustrates sources of image distortion.

A preliminary #3DReconstruction video is an early draft of a scene reconstruction made from whatever evidence is available so far like measurements, photos, video, and sometimes 3D scans. It turns that information into a computer model and can animate where people, vehicles, and camera views may have been. “Preliminary” means it can change as more evidence comes in and as experts verify what is accurate. In a #Trial, this can be powerful because it helps a jury see distances, timing, lines of sight, and whether a person could realistically perceive a threat. It can also help lawyers test claims and spot missing facts. But it only helps if it is grounded in real evidence and explained by a qualified expert, because the other side can challenge the assumptions and a judge can keep it out if it is misleading or unfairly prejudicial. #ReneeNicoleGood #ICE #minnesota Credit: @index.ngo @misanharriman

As a core step in structure-from-motion and SLAM, robust feature detection and description under challenging scenarios such as significant viewpoint changes remain unresolved despite their ubiquity. While recent works have identified the importance of local features in modeling geometric transformations, these methods fail to learn the visual cues present in long-range relationships. We present Robust Deformable Detector (RDD), a novel and robust keypoint detector/descriptor leveraging the deformable transformer, which captures global context and geometric invariance through deformable self-attention mechanisms. Specifically, we observed that deformable attention focuses on key locations, effectively reducing the search space complexity and modeling the geometric invariance. Furthermore, we collected an Air-to-Ground dataset for training in addition to the standard MegaDepth dataset. Our proposed method outperforms all state-of-the-art keypoint detection/description methods in sparse m