VIN-NBV: A View Introspection Network for Next-Best-View Selection
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_ | 1866909752066310144 |
|---|---|
| author | Frahm, Noah Zhao, Dongxu Beltran, Andrea Dunn Alterovitz, Ron Frahm, Jan-Michael Oliva, Junier Sengupta, Roni |
| author_facet | Frahm, Noah Zhao, Dongxu Beltran, Andrea Dunn Alterovitz, Ron Frahm, Jan-Michael Oliva, Junier Sengupta, Roni |
| contents | Next Best View (NBV) algorithms aim to maximize 3D scene acquisition quality using minimal resources, e.g. number of acquisitions, time taken, or distance traversed. Prior methods often rely on coverage maximization as a proxy for reconstruction quality, but for complex scenes with occlusions and finer details, this is not always sufficient and leads to poor reconstructions. Our key insight is to train an acquisition policy that directly optimizes for reconstruction quality rather than just coverage. To achieve this, we introduce the View Introspection Network (VIN): a lightweight neural network that predicts the Relative Reconstruction Improvement (RRI) of a potential next viewpoint without making any new acquisitions. We use this network to power a simple, yet effective, sequential samplingbased greedy NBV policy. Our approach, VIN-NBV, generalizes to unseen object categories, operates without prior scene knowledge, is adaptable to resource constraints, and can handle occlusions. We show that our RRI fitness criterion leads to a ~30% gain in reconstruction quality over a coverage-based criterion using the same greedy strategy. Furthermore, VIN-NBV also outperforms deep reinforcement learning methods, Scan-RL and GenNBV, by ~40%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VIN-NBV: A View Introspection Network for Next-Best-View Selection Frahm, Noah Zhao, Dongxu Beltran, Andrea Dunn Alterovitz, Ron Frahm, Jan-Michael Oliva, Junier Sengupta, Roni Computer Vision and Pattern Recognition Robotics I.2.10; I.2.9 Next Best View (NBV) algorithms aim to maximize 3D scene acquisition quality using minimal resources, e.g. number of acquisitions, time taken, or distance traversed. Prior methods often rely on coverage maximization as a proxy for reconstruction quality, but for complex scenes with occlusions and finer details, this is not always sufficient and leads to poor reconstructions. Our key insight is to train an acquisition policy that directly optimizes for reconstruction quality rather than just coverage. To achieve this, we introduce the View Introspection Network (VIN): a lightweight neural network that predicts the Relative Reconstruction Improvement (RRI) of a potential next viewpoint without making any new acquisitions. We use this network to power a simple, yet effective, sequential samplingbased greedy NBV policy. Our approach, VIN-NBV, generalizes to unseen object categories, operates without prior scene knowledge, is adaptable to resource constraints, and can handle occlusions. We show that our RRI fitness criterion leads to a ~30% gain in reconstruction quality over a coverage-based criterion using the same greedy strategy. Furthermore, VIN-NBV also outperforms deep reinforcement learning methods, Scan-RL and GenNBV, by ~40%. |
| title | VIN-NBV: A View Introspection Network for Next-Best-View Selection |
| topic | Computer Vision and Pattern Recognition Robotics I.2.10; I.2.9 |
| url | https://arxiv.org/abs/2505.06219 |