OccamView: Object-Conditioned View Selection for Frame-Budgeted Active 3D Gaussian Reconstruction

• URL: http://arxiv.org/abs/2608.16499v1
• Date: Mon, 17 Aug 2026 12:38:56 GMT
• Title: OccamView: Object-Conditioned View Selection for Frame-Budgeted Active 3D Gaussian Reconstruction
• Abstract summary: OccamView is an object-conditioned view-selection framework for frame-budgeted active 3D Gaussian reconstruction.<n>We introduce a Geo-Floor mechanism that restricts object-conditioned re-ranking to geometrically competitive candidates.<n>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.
• Score: 31.50146089752062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection [75.77856258186854]
  360 salient object detection aims to accurately segment salient regions across a full field of view.<n>Existing methods mainly focus on compensating projection distortion while overlooking the interaction between panoramic geometry and salient object structure.<n>We propose SphereSOD, an ERP-native framework that couples panoramic geometry with evolving salient structures.
  arXiv  Detail & Related papers  (2026-09-07T14:47:54Z)
• G$^2$TAM: Geometry Grounded Track Anything Model [49.05553245076823]
  We propose a unified framework for promptable instance tracking in 3D using only unordered RGB images or videos.<n>G$2$TAM employs spatially aligned geometric representations as implicit memory, ensuring stable instance identity and localization across frames and views.<n>At its core is a cross-modal spatial encoder that integrates visual and textual prompts into a shared geometric space, enabling end-to-end spatial reconstruction and instance-consistent mask prediction.
  arXiv  Detail & Related papers  (2026-07-04T09:32:13Z)
• MAGE: View-guided Point Cloud Completion with Efficient Modality Alignment and Adaptive Geometry Enhancement [19.44604826924269]
  View-based point cloud completion aims to recover a complete 3D shape from a partial point cloud, guided by a single-view image.<n>Existing approaches often suffer from limited performance due to weak modality alignment and limited self-geometry enhancement.<n>We propose a unified geometry-aware framework that integrates efficient modality alignment and adaptive geometry enhancement.
  arXiv  Detail & Related papers  (2026-06-30T05:25:29Z)
• GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval [54.741349848771144]
  GeoMamba is a geometry-driven framework for optical-SAR fine-grained retrieval.<n>GFI module enhances cross-modal feature interaction and incorporates structural priors.<n>GeoMamba outperforms existing methods, achieving 63.3% mAP and 77.0% Rank-1 accuracy in all-to-all retrieval setting.
  arXiv  Detail & Related papers  (2026-05-19T12:08:09Z)
• GSMem: 3D Gaussian Splatting as Persistent Spatial Memory for Zero-Shot Embodied Exploration and Reasoning [9.203056739430865]
  GSMem is a zero-shot embodied exploration and reasoning framework built upon 3D Gaussian Splatting (3DGS)<n>By explicitly parameterizing continuous geometry and dense appearance, 3DGS serves as a persistent spatial memory that endows the agent with textitSpatial Recollection<n>We introduce a hybrid exploration strategy that combines VLM-driven semantic scoring with a 3DGS-based coverage objective, balancing task-aware exploration with geometric coverage.
  arXiv  Detail & Related papers  (2026-03-19T16:55:54Z)
• Affostruction: 3D Affordance Grounding with Generative Reconstruction [50.586896835836164]
  We propose Affostruction, a generative framework that reconstructs complete geometry from partial observations and grounds affordances on the full shape.<n>Affostruction achieves 19.1 aIoU on affordance grounding and 32.67 IoU for 3D reconstruction, enabling accurate affordance prediction on complete shapes.
  arXiv  Detail & Related papers  (2026-01-14T06:33:12Z)
• Geometry-Editable and Appearance-Preserving Object Compositon [67.98806888489385]
  General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties.<n>Recent approaches derive semantic embeddings and integrate them into advanced diffusion models to enable geometry-editable generation.<n>We introduce a Disentangled Geometry-editable and Appearance-preserving Diffusion model that first leverages semantic embeddings to implicitly capture desired geometric transformations.
  arXiv  Detail & Related papers  (2025-05-27T09:05:28Z)
• BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation [81.24160191975781]
  This paper presents a general RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings.<n>To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose.<n>The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point datasets.
  arXiv  Detail & Related papers  (2025-04-10T17:58:35Z)
• Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion [16.379647695019308]
  3D scene reconstruction is a foundational problem in computer vision.<n>We introduce the Gaussian Object Carver (GOC), a novel, efficient, and scalable framework for object-compositional 3D scene reconstruction.<n>GOC leverage 3D Gaussian Splatting (GS), enriched with monocular geometry priors and multi-view geometry regularization, to achieve high-quality and flexible reconstruction.
  arXiv  Detail & Related papers  (2024-12-03T01:34:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.