During the Gaussian Splatting optimization process, the scene’s geometry can gradually deteriorate if its structure is not deliberately preserved, especially in non-textured regions such as walls, ceilings, and furniture surfaces. This degradation significantly affects the rendering quality of novel views that deviate significantly from the viewpoints in the training data. To mitigate this issue, we propose a novel approach called GeoGaussian. Based on the smoothly connected areas observed from point clouds, this method introduces a novel pipeline to initialize thin Gaussians aligned with the surfaces, where the characteristic can be transferred to new generations through a carefully designed densification strategy. Finally, the pipeline ensures that the scene’s geometry and texture are maintained through constrained optimization processes with explicit geometry constraints.

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In this paper, we present ShapeMaker, a unified selfsupervised learning framework for joint shape canonicalization, segmentation, retrieval and deformation. Given a partially-observed object in an arbitrary pose, we first canonicalize the object by extracting point-wise affineinvariant features, disentangling inherent structure of the object with its pose and size. These learned features are then leveraged to predict semantically consistent part segmentation and corresponding part centers. Next, our lightweight retrieval module aggregates the features within each part as its retrieval token and compare all the tokens with source shapes from a pre-established database to identify the most geometrically similar shape. Finally, we deform the retrieved shape in the deformation module to tightly fit the input object by harnessing part center guided neural cage deformation.

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*This paper introduces a new benchmark dataset, Open-Structure, for evaluating visual odometry and SLAM methods, which directly equips point and line measurements, correspondences, structural associations, and co-visibility factor graphs instead of providing raw images. Based on the proposed benchmark dataset, these 2D or 3D data can be directly input to different stages of SLAM pipelines to avoid the impact of the data preprocessing modules in ablation experiments.*

office0: img | observation | initial factor graph |
livingroom0: img | observation | initial factor graph |
---|---|

office1: img |observation | initial factor graph |
livingroom1: img | observation | initial factor graph |

office2: img | observation | initial factor graph |
livingroom2: img | observation | initial factor graph |

office3: img | observation | initial factor graph |
livingroom3: img | observation | initial factor graph |

nostr_textrue_far: img | observation | initial factor graph |
box1: img | observation | initial factor graph |
---|---|

nostr_texture_near_loop: img | observation | initial factor graph |
box2: img | observation | initial factor graph |

str_notexture_far: img | observation | initial factor graph |
corridor1: img | observation | initial factor graph |

str_notexture_near: img | observation |initial factor graph |
corridor2: img | observation | initial factor graph |

str_texture_near: img | observation | initial factor graph |
sphere1: img | observation | initial factor graph |

str_texture_far: img | observation | initial factor graph |
sphere2: img | observation | initial factor graph |

hospital: img observation initial factor graph |
japanesealley: img observation initial factor graph |
---|---|

carwelding: img observation initial factor graph |
office: img observation initial factor graph |

```
@inproceedings{liopenstructure,
author = {Li, Yanyan and Guo, Zhao and Yang, Ze and Sun Yanbiao and Liang, Zhao and Tombari, Federico},
title = {Open-Structure: a Structural Benchmark Dataset for SLAM Algorithms},
year = {2023},
booktitle = {arXiv}
}
```

A new minimal solution is proposed to solve relative rotation estimation between two images without overlapping areas by exploiting a new graph structure, which we call Extensibility Graph (E-Graph). Differently from a co-visibility graph, high-level landmarks, including vanishing directions and plane normals, are stored in our E-Graph, which are geometrically extensible. Based on E-Graph, the rotation estimation problem becomes simpler and more elegant, as it can deal with pure rotational motion and requires fewer assumptions, e.g. Manhattan/Atlanta World, planar/vertical motion. Finally, we embed our rotation estimation strategy into a complete camera tracking and mapping system which obtains 6-DoF camera poses and a dense 3D mesh model.

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In this paper, a robust RGB-D SLAM system is proposed to utilize the structural information in indoor scenes, allowing for accurate tracking and efficient dense mapping on a CPU. Prior works have used the Manhattan World (MW) assumption to estimate low-drift camera pose, in turn limiting the applications of such systems. This paper, in contrast, proposes a novel approach delivering robust tracking in MW and non-MW environments. We check orthogonal relations between planes to directly detect Manhattan Frames, modeling the scene as a Mixture of Manhattan Frames.

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This work proposes a RGB-D SLAM system specifically designed for structured environments and aimed at improved tracking and mapping accuracy by relying on geometric features that are extracted from the surrounding. Structured environments offer, in addition to points, also an abundance of geometrical features such as lines and planes, which we exploit to design both the tracking and mapping components of our SLAM system.

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The low-drift monocular SLAM method, Structure-SLAM, is proposed targeting indoor scenarios, where monocular SLAM often fails due to the lack of textured surfaces. Our approach decouples rotation and translation estimation of the tracking process to reduce the long-term drift in indoor environments. In order to take full advantage of the available geometric information in the scene, surface normals are predicted by a convolutional neural network from each input RGB image in real-time.

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