← Back to all news
arXiv Computer Vision By Fengcheng Yu, Haoran Xu, Canming Xia, Ziyang Zong, Guang Tan

Deflickering Vision-Based Occupancy Networks through Lightweight Spatio-Temporal Correlation

Read the original on arXiv Computer Vision →

The paper introduces OccLinker, a lightweight plugin for vision‑based occupancy networks that reduces flickering by efficiently merging historical static and motion cues with current features via a dual cross‑attention mechanism. It generates correction components to refine base network predictions and proposes a new temporal consistency metric to quantify flickering. Experiments on two benchmark datasets show that OccLinker improves performance with minimal computational overhead while effectively diminishing flickering artifacts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Oct 23

Foundation4D: Towards a unified perception and reconstruction model for autonomous vehicles

Foundation4D is a unified feed‑forward model that performs geometry estimation, 3D detection, semantic occupancy, and Gaussian reconstruction from multi‑camera driving video. It uses a shared backbone with factorized spatiotemporal attention to merge observations across cameras and time, and a video inference pipeline that refines camera trajectories via pose graph optimization and assembles object tracks. The authors also provide a data annotation pipeline and release the PAI‑Foundation4D benchmark, achieving state‑of‑the‑art results on depth, detection, and occupancy metrics and demonstrating that co‑training detection with geometry and occupancy improves performance.

By NVIDIA, :, S\'ergio Agostinho, Maximilian Apodaca, Emmanuel Attia, Guillem Braso, George Dong, Ilya Fedorov, Michele Fenzi, Gang Huang, Jiahui Huang, Shengyu Huang, Bingxin Ke, Laura Leal-Taix\'e, Kuan-Ting Lee, Mingxuan Liu, Tian Liu, Tim Meinhardt, Aljo\v{s}a O\v{s}ep, Naveen Kumar Rai, Kefei Ren, Cristiano Saltori, Keqiang Wu, Ning Xu, Fei Xue, Jin-Bey Yu, Lei Zhang, Qunjie Zhou
agentsfine-tuningbenchmarks
More like this →
arXiv Computer Vision
Sep 24

RoadOcc Learns When to Persist, Transport, or Refresh Memory for Roadside Occupancy Prediction

RoadOcc is a new method for roadside occupancy prediction that learns to route information among three memory sources: Persist (fixed-coordinate history), Transport (velocity-addressed history), and Refresh (current evidence). It employs dynamic-aware cross‑attention, multi‑scale voxel velocity estimation, and velocity‑guided dynamic sparse fusion to combine these sources efficiently. On the InfraOcc dataset, RoadOcc achieves 65.29 mIoU and 32.37 dynamic mIoU, outperforming the previous STCOcc baseline by significant margins.

By Xiaokai Bai, Lei Yang, Songkai Wang, Lianqing Zheng, Si-Yuan Cao, Hui-liang Shen
More like this →