arXiv Computer Vision
FreeOcc: Decoupling Ego-Motion for Efficient 4D Occupancy Forecasting via Continuous Flow Matching
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arXiv Computer Vision
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UFMF: Radar-Guided Kinematic Priors for Real-Time 4D Occupancy Forecasting
UFMF (Ultra-Fast Multi-modal Forecaster) is a real‑time 4D occupancy forecasting model that uses a lightweight diagonal state‑space core and a radar‑guided velocity‑aware dynamic prior to predict future motion. The prior splits future displacement into a linear extrapolation and a learned residual, enabling per‑cell flow fields that carry a compact dynamic latent subspace during rollout. With auxiliary world‑frame dynamic supervision and class‑conditioned vertical‑profile supervision, UFMF achieves 122 FPS on an RTX 4090 and outperforms recent occupancy world models on nuScenes while using far less computational resources.
arXiv Computer Vision
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LighTROcc: Lightweight 4D Occupancy Forecasting via Instance-Centric 3D Gaussians
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...
arXiv Computer Vision
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Tight Learned Event-Inertial Odometry (TLEIO): a Transformer-Based Architecture for End-to-End Motion from Event Streams
arXiv:2610.33356v1 Announce Type: new Abstract: Event cameras provide microsecond temporal resolution, high dynamic range, and sparse asynchronous measurements, making them compelling for odometry un...
arXiv AI
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WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving
arXiv:2608.20974v1 Announce Type: cross Abstract: Video Joint Embedding Predictive Architecture (V-JEPA) learns powerful spatiotemporal representations from video through self-supervised latent featu...
arXiv Computer Vision
Sep 23
VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding
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...
arXiv Machine Learning
Oct 28
Real-Time Human Trajectory Prediction from RGB-D for Mobile Robots Using Bi-Mamba
The paper introduces TrajMamba, a lightweight bidirectional state‑space model that predicts short‑term human trajectories from RGB‑D cameras for mobile robots. It aligns training and deployment pipelines by sharing a processing path for pose extraction, depth back‑projection, and smoothing, and encodes recent hip‑center velocities with stacked bidirectional Mamba blocks before decoding future velocities with a GRU head. On the ETH/UCY benchmark, TrajMamba achieves an average ADE of 0.29 m and FDE of 0.51 m, runs in real time on modest hardware, and has been deployed on a TIAGo robot equipped with an NVIDIA Jetson Orin.