VEOcc++ is a voxel‑centric framework that unifies online incremental semantic occupancy mapping with offline global map refinement by maintaining a persistent sparse voxel feature map. During exploration the map expands and integrates local features with reliability‑guided updates, while a context‑aware decoder produces the current semantic occupancy map. After exploration, cached local features are consolidated and combined with the online representation for multi‑scale spatial refinement, achieving state‑of‑the‑art IoU and mIoU scores on EmbodiedOcc‑ScanNet and demonstrating zero‑shot transfer to unseen environments on a mobile robot.
By Ruoyu Wang, Jiahan Li, Yong Liu, Kexin Zhang
arXiv:2610.00188v1 Announce Type: cross
Abstract: Voxel-based volumetric mapping is fundamental to 3D reconstruction, yet fixed-resolution grids remain inherently inefficient - wasting memory in unif...
By Alpay Ozkan, Tunc Ozan Aydin, Marc Pollefeys, Jelena Trisovic, Daniel Barath
The paper studies how open‑vocabulary segmentation models perform on UAV footage, focusing on temporal consistency of predictions. By linking frame‑wise outputs to persistent 3D voxels via metric fusion, the authors propose a voxel‑level evaluation that measures final agreement, Semantic Belief Drift, Observation Persistence, and uncertainty. Experiments on UAVid‑3D show that high overall agreement can mask instability when observations are sparse, and that persistence‑stratified analysis reveals greater disagreement for recurrent voxels while belief drift reduces with more evidence.
By Saurbh Singh Jamwal
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
The paper introduces Out-of-Distribution Semantic Occupancy Prediction, a task that focuses on detecting unknown objects in 3D voxel space for autonomous driving. It proposes Realistic Anomaly Augmentation to create two new datasets, VAA-KITTI and VAA-KITTI-360, and presents the OccOoD framework, which uses Cross‑Space Semantic Refinement to improve OoD detection while maintaining semantic occupancy accuracy. Experiments show OccOoD achieves an AuROC of 65.50% and an AuPRCr of 31.83% within a 1.2 m radius, demonstrating strong generalization to real‑world urban scenes.
By Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Ruiping Liu, Fei Teng, Kai Luo, Zhiyong Li, Kailun Yang
PerSeM is a training‑free framework that builds a persistent semantic memory for long‑horizon UAV mapping by associating frame‑wise segmentation results with world‑space voxels and refining them through spatial refinement, trust‑aware replay, and context‑guided verification. Experiments on Forest and UAVScenes benchmarks show that this persistent 3D memory improves semantic correctness and temporal stability compared to frame‑wise predictions, especially in semantically difficult and temporally unstable regions. The method achieves these gains without retraining or additional neural‑network inference.
By Saurbh Singh Jamwal, Ganesh Ramakrishnan