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
arXiv:2610.09444v1 Announce Type: new
Abstract: Forecasting future 3D occupancy from surround-view cameras is essential for autonomous driving, yet existing approaches rely on dense voxel or bird's-e...
By Hwanhee Jung, SeungHyeon Kim, Inkyu Koo, Qixing Huang, Sang Ho Yoon, Sangpil Kim
SplatLabel is an automated pipeline that uses a 4D Gaussian representation to generate LiDAR segmentation and semantic occupancy grids with predictive confidence. It models dynamic scenes through an explicit temporal manifold, tracking moving actors without requiring pre‑annotated 3D bounding boxes. By integrating 360‑degree LiDAR depth maps and distilling soft probabilities from 2D models, it resolves semantic ambiguities over time and space, and evaluates pseudo‑labels via a selective classification framework that balances precision and recall.
By Nitya Nanvani, Andras Palffy, Holger Caesar
arXiv:2608.13147v2 Announce Type: replace
Abstract: Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera str...
By Longfei Xu, Xiaohui Wang, Zehao Huang, Han Li, Ya Yang, Naiyan Wang, Si Liu
arXiv:2609.08636v1 Announce Type: cross
Abstract: Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize...
By Qiaohui Chu, Haoyu Zhang, Meng Liu, Haoxiang Shi, Dongmei Jiang, Liqiang Nie
AffordDrive3D is a world-action model that jointly learns future action-relevant regions and spatial geometry for autonomous driving. It uses a VLM backbone to forecast drivable areas and collision-critical regions while predicting future geometry from RGB world-model latents. On the NAVSIM benchmark, it achieves state‑of‑the‑art performance with 91.3 PDMS and 89.9 EPDMS, showing the benefit of combining affordance prediction with geometry modeling for trajectory planning.
By Tianhui Cai, Xinglong Sun, Chao Fang, Zhenxin Li, Rui Song, Jose M. Alvarez, Yunxiang Mao, Jiaqi Ma, Langechuan Liu