arXiv:2605.25059v4 Announce Type: replace
Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
By Ruoyu Wang, Yong Liu, Jiahan Li, Sheng Tao, Yuhang Lin, Yukai Ma
VoxelFix is a graph‑based post‑hoc semantic correction method that refines voxel labels in completed 3D voxel maps while preserving their geometry and occupancy. It learns to correct errors by exploiting local geometry and neighboring semantic information, using training pairs generated by corrupting annotated maps with class confusions from upstream perception pipelines. Experiments on OccuFly maps show consistent improvements of 4.23–5.00 percentage points in mIoU, especially for tree, roof, and wall classes, and the method generalizes to out‑of‑distribution aerial scenes.
By Sunesh Praveen Raja Sundarasami, Taehyoung Kim, Johannes Scherer, Toma\v{z} Coti\v{c}, Sivasubiramaniam Subbiah, Andreas Greiner, Paul Spannaus, Sebastian Houben
O3N is a novel framework that performs open‑vocabulary occupancy prediction from a single omnidirectional RGB image. It introduces a polar‑spiral voxel embedding (PsM) for continuous 360° spatial representation, an Occupancy Cost Aggregation (OCA) module that unifies geometric and semantic supervision, and a Natural Modality Alignment (NMA) pathway that aligns visual, voxel, and text features. Experiments show state‑of‑the‑art results on QuadOcc and Human360Occ benchmarks, with strong cross‑scene generalization and semantic scalability.
By Mengfei Duan, Hao Shi, Fei Teng, Guoqiang Zhao, Yuheng Zhang, Zhiyong Li, Kailun Yang
The paper investigates how different 2D image backbones affect indoor semantic occupancy prediction in RGB‑D pipelines. By keeping the projection, depth branch, and occupancy head constant and swapping only the image encoder, the authors find that stronger backbones such as DINOv2 and BLIP2 significantly raise mIoU scores compared to the default ResNet‑50. These results show that the choice of image backbone is a major determinant of 3D occupancy accuracy, outweighing many specialized 3D modules.
By Shizhang Fanga, Wanling Yea, Qi Zheng
Reading the Room (RR) is a global‑local method that registers scale‑ambiguous, sparse semantic VSLAM maps to partially built floor plans. It introduces a single robust, differentiable, class‑balanced semantic‑geometric energy function that drives all pipeline stages, and offers two global initialization schemes—one using a single user‑provided correspondence and another using a cross‑modal matching network. Evaluated on 33 sequences from three construction sites, RR outperforms classical and learning‑based baselines, improving mean absolute trajectory error by 4.5× overall and 7.6× when an anchor is supplied.
By Bjorn Stragier, Emilia Szyma\'nska, J. Micah Prendergast, Lukas Rosenberger Schmid
arXiv:2609.38864v1 Announce Type: new
Abstract: Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as...
By Jinglong Wang, Yunjie Wang, Zhiyang Zhang, Jiawei He, Ye Yuan, Bo Qiu, Jing Zhang