Building temporally coherent 3D maps with VGGT for memory-efficient Semantic SLAM
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917107257573376 |
|---|---|
| author | Dinya, Gergely Halász, Péter Lőrincz, András Karacs, Kristóf Gelencsér-Horváth, Anna |
| author_facet | Dinya, Gergely Halász, Péter Lőrincz, András Karacs, Kristóf Gelencsér-Horváth, Anna |
| contents | We present a fast, spatio-temporal scene understanding framework based on Visual Geometry Grounded Transformer (VGGT). The proposed pipeline is designed to enable efficient, close to real-time performance, supporting applications including assistive navigation. To achieve continuous updates of the 3D scene representation, we process the image flow with a sliding window, aligning submaps, thereby overcoming VGGT's high memory demands. We exploit the VGGT tracking head to aggregate 2D semantic instance masks into 3D objects. To allow for temporal consistency and richer contextual reasoning the system stores timestamps and instance-level identities, thereby enabling the detection of changes in the environment. We evaluate the approach on well-known benchmarks and custom datasets specifically designed for assistive navigation scenarios. The results demonstrate the applicability of the framework to real-world scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Building temporally coherent 3D maps with VGGT for memory-efficient Semantic SLAM Dinya, Gergely Halász, Péter Lőrincz, András Karacs, Kristóf Gelencsér-Horváth, Anna Computer Vision and Pattern Recognition We present a fast, spatio-temporal scene understanding framework based on Visual Geometry Grounded Transformer (VGGT). The proposed pipeline is designed to enable efficient, close to real-time performance, supporting applications including assistive navigation. To achieve continuous updates of the 3D scene representation, we process the image flow with a sliding window, aligning submaps, thereby overcoming VGGT's high memory demands. We exploit the VGGT tracking head to aggregate 2D semantic instance masks into 3D objects. To allow for temporal consistency and richer contextual reasoning the system stores timestamps and instance-level identities, thereby enabling the detection of changes in the environment. We evaluate the approach on well-known benchmarks and custom datasets specifically designed for assistive navigation scenarios. The results demonstrate the applicability of the framework to real-world scenarios. |
| title | Building temporally coherent 3D maps with VGGT for memory-efficient Semantic SLAM |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16282 |