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arXiv Computer Vision

VoxelFix: Post-Hoc Semantic Correction of Completed 3D Voxel Maps

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.

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arXiv Computer Vision
Sep 1

Understanding Temporal Semantic Stability in Open-Vocabulary UAV Perception through Metric 3D Fusion

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
computer-vision
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arXiv AI
Aug 11

RAG-3DSG: Enhancing 3D Scene Graphs with Re-Shot Guided Retrieval-Augmented Generation

arXiv:2601. 10168v3 Announce Type: replace-cross Abstract: Open-vocabulary 3D Scene Graph (3DSG) can enhance various downstream tasks in robotics by leveraging structured semantic representations, yet current 3DSG construction methods suffer from semantic inconsistencies caused by noisy cross-image aggregation under occlusions and constrained viewpoints.

By Yue Chang, Rufeng Chen, Zhaofan Zhang, Yi Chen, Yifan Tian, Sihong Xie
llmsragroboticsmultimodalbenchmarks
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arXiv Computer Vision
Aug 26

O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Embodied Intelligent Robotics

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
ragroboticsbenchmarkssafety
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Hugging Face Trending Papers
Aug 6

Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments

Hierarchical 3D scene graphs are a promising representation for high-level spatial reasoning in autonomous mobile platforms. However, existing extraction frameworks typically rely on purely local visual clustering or strict geometric heuristics, such as wall-separated rooms, which fail in open-plan or arbitrarily-structured environments.

llmsagentscomputer-visionroboticsbenchmarkssafety
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