OccamView: Object-Conditioned View Selection for Frame-Budgeted Active 3D Gaussian Reconstruction
2026-08-17 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors focus on improving how 3D scenes are actively scanned by choosing where to look next when you have only a limited number of chances to capture views. Instead of treating all hidden areas equally, their method, OccamView, uses object detection to remember where objects are and focuses on those spots to get better details. They also use a special scoring system to pick viewpoints that reduce blockage and keep efficient exploration. Tests show this approach helps complete object reconstructions better, especially when the viewing budget is tight.
3D Gaussian reconstructionnext-best view selectionRGB-D sensingobject detectionoccupancy mappingocclusion-aware scoringscene reconstructionactive visionframe budgetviewpoint planning
Authors
Hongbo Gao, Wei Zhang, Zeyu Ni, Dihao Zhu, Ruifeng Li, Yunke Wang, Chang Xu
Abstract
Active 3D Gaussian reconstruction fundamentally relies on selecting informative next-best views under limited sensing budgets. Existing active 3DGS methods primarily plan viewpoints according to geometric information gain, treating object-induced hidden regions in the same manner as general unexplored space. Under tight frame budgets, such geometry-driven strategies may prioritize global scene coverage while leaving partially observed objects incompletely reconstructed. To address this limitation, we propose OccamView, an object-conditioned view-selection framework for frame-budgeted active 3D Gaussian reconstruction. Rather than predicting unseen object geometry or performing shape completion, OccamView maintains an online object memory from open-vocabulary detections grounded in measured RGB-D observations and represents unresolved local occupancy around detected objects as conservative hidden-region proxies. Candidate viewpoints are then evaluated using an occlusion-aware proxy-coverage score. Furthermore, we introduce a Geo-Floor mechanism that restricts object-conditioned re-ranking to geometrically competitive candidates, allowing object-conditioned cues to guide complementary observations while preserving the geometry-driven exploration behavior of the underlying planner. Experiments on Replica and Matterport3D under a unified frame-budgeted protocol show that OccamView consistently reduces Completion and improves Completion Ratio across five frame budgets, with particularly pronounced gains under limited frame budgets. These results demonstrate that lightweight object-conditioned cues effectively complement geometry-driven active view planning.