arXiv:2610.34093v1 Announce Type: new Abstract: Open-vocabulary 3D occupancy prediction is important for embodied agents operating in complex environments whose semantic categories extend beyond fixe...
arXiv:2610.35312v1 Announce Type: new Abstract: Monocular occupancy prediction in indoor scenes aims to recover a scene's 3D geometry and semantics from a single RGB image. Existing methods are restr...
OccAnyScene introduces a unified approach for 3D occupancy prediction across both indoor and outdoor scenes, addressing the challenge of handling diverse camera setups, spatial ranges, voxel specifications, and semantic taxonomies. The method builds on a pretrained depth model, using pixel-aligned frustum feature aggregation and frustum-parameterized Gaussian construction to generate scene-adaptive occupancy predictions. It achieves state-of-the-art performance, scoring 59.92% mIoU on Occ-ScanNet and 23.06% mIoU on SurroundOcc-nuScenes.
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.
arXiv:2610.28530v1 Announce Type: new Abstract: Collaborative perception enables agents to retrieve the scene semantics and geometry beyond the field of view of any individual agent. This broader sce...
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.
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...
Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as it can model holistic 3D spaces by encoding geome...
arXiv:2609.09012v2 Announce Type: replace Abstract: Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, where...
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.
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...
FM-Occ introduces two novel components for 3D occupancy prediction: 3D Sparse Subvoxel Shuffle (3S) to diversify features during upsampling, and Entropy-Regulated Layer-wise Memory (ER‑LM) to preserve semantic information across decoder layers. 3S reduces feature homogenization by creating and rearranging child features on a finer sparse grid, while ER‑LM uses gated recurrent updates and entropy‑based regulation to maintain a memory of semantic predictions. On SemanticKITTI, FM‑Occ improves mIoU from 12.16% to 12.73% with minimal memory cost, and further boosts BEV‑based DISC performance from 16.78% to 17.17%.