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.
arXiv:2605. 20301v2 Announce Type: replace-cross Abstract: In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making.
arXiv:2610.41834v1 Announce Type: new Abstract: Accurate BEV map prediction is a critical component of autonomous driving systems, enabling downstream planning and control. Recent approaches such as...
arXiv:2608.21055v1 Announce Type: cross Abstract: Collaborative perception extends the sensing range of a single vehicle by fusing observations from nearby agents, which improves the robustness of au...
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.
arXiv:2603. 24016v2 Announce Type: replace-cross Abstract: Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects.
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.
arXiv:2608. 03244v1 Announce Type: new Abstract: Image-goal visual navigation is a fundamental capability for embodied agents.
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts.
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...
The paper presents a lightweight, training‑free framework for real‑time unsupervised object discovery from asynchronous event camera streams. It introduces a linear‑time Spatio‑Temporal Probabilistic Event Filter (SPEF) that adaptively distinguishes salient motion from noise, and an Event Morton Code Clustering (EMCC) module that efficiently groups events without costly distance calculations. Experiments on E‑MLB, FRED, and eTraM datasets show SPEF outperforms classical filters and competes with learning‑based methods, while EMCC achieves the highest accuracy and fastest execution among density‑based clustering baselines.
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...
arXiv:2606. 07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections.