OPUS: Occupancy Prediction Using a Sparse Set 14 Sep 2024 · Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, Ming-Ming Cheng · Edit social preview Occupancy prediction, aiming at predicting the occupancy status within voxelized 3D environment, ...
OPUS: Occupancy Prediction Using a Sparse Setarxiv.org/abs/2409.09350 https://github.com/jbwang1997/OPUSgithub.com/jbwang1997/OPUS 在OPUS 中,我们提出了一种全新的 Occupancy 预测范式。OPUS 通过预测一组带有语义标签的点集来自适应的定位 Occupied Voxels,从而构建出 3D 场景的几何形状。由于模型...
[NeurIPS 2024] OPUS: Occupancy Prediction Using a Sparse Set deep-learningtransformerautonomous-drivingoccupancy-prediction UpdatedFeb 16, 2025 Python AlphaPlusTT/DAOcc Star48 Code Issues Pull requests DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction ...
原标题:OPUS: Occupancy Prediction Using a Sparse Set 论文链接:https://arxiv.org/pdf/2409.09350代码链接:https://github.com/jbwa… 阅读全文 赞同 9 添加评论 分享 收藏 浅谈Occupancy 地平线开发者 已认证账号 研究意义: Occupancy Network 算法可以更好的克服感知任务中存在的...
2024 NeurIPS OPUS: Occupancy Prediction Using a Sparse Set Code 2024 ECCV ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D Occupancy Perception via View-Guided Transformers Code 2024 ECCV CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction Code 2024 ECCV VEON: Vocabulary-...
Occupancy prediction plays a pivotal role in autonomous driving. Previous methods typically construct dense 3D volumes, neglecting the inherent sparsity of the scene and suffering high computational costs. To bridge the gap, we introduce a novel fully sparse occupancy network, termed SparseOcc. Sparse...
Urban energy prediction with mobile occupancy Using the previously described UBEM we now run simulations for the 1,266 modeled buildings under each of the three scenarios (DOE reference, low-impact and high-impact), each with occupancy distributions forμ = 0,μ = 0.5, andμ = ...
However, because we were interested in using realistic reserve sizes and realistically low population densities of Asian bears, we assumed the data sets for these populations will be too sparse to reliably fit the more complex models that can account for spatial correlation. We investigated ...
DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data. In: Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments. pp. 1–10. Google Scholar Arvidsson et al., 2021 Arvidsson S., Gullstrand M., Sirmacek B.,...
The use of a separate team of landmark robots enables the mapper robots to remain decentralized while still being directed toward the unmapped areas of the environment. 2.2. Topological Mapping Using Swarms with Limited Exteroceptive-Sensing Topological maps are a sparse representation of an ...