The paper introduces RGBTR‑Motion, a new benchmark that synchronizes RGB, thermal, and radar data with dense moving‑instance masks and consistent identities for surveillance scenes. It also presents SAM‑Radar, a segmentation and tracking framework that fuses calibrated RGBT features with radar returns, using radar‑aware detection and motion supervision to reject clutter and maintain identity continuity during low visibility or occlusion. SAM‑Radar achieves state‑of‑the‑art performance, improving IoU, F1‑50, MOTA, HOTA, and IDF1 metrics over existing methods.
arXiv:2609.22857v1 Announce Type: new Abstract: Dynamic object segmentation and ego-motion estimation are closely coupled problems in autonomous driving, as accurate ego-motion estimation typically r...
WildDepth‑v2 expands the original RGB‑LiDAR WildDepth dataset by adding 37 RGB‑LiDAR‑4D‑radar sequences, a frozen hidden test set for RGB‑only depth estimation and RGB‑plus‑sparse‑LiDAR depth completion, and metric 3D trajectories derived from 2D tracks using LiDAR, radar, or monocular depth. The dataset demonstrates that object‑level radar calibration improves monocular depth accuracy (AbsRel 0.120 vs. 0.198) and that once‑per‑track radar scaling reduces 3D position error four‑fold, matching the performance of LiDAR calibration. Additionally, transferring radar Doppler onto LiDAR yields denser motion masks, lowers LiDAR accumulation error, and provides radial speed estimates. whyItMatters":"The dataset supplies the first imaging‑radar wildlife recordings and a benchmark that enables metric depth, motion, and tracking research in ecological contexts, filling a gap where monocular video lacks scale and existing wildlife datasets lack metric ground truth."
DyRAD introduces a novel radar novel‑view synthesis framework that models dynamic driving scenes by separating static background reflectors from motion‑tracked dynamic point reflectors, enabling the rendering of full range‑azimuth‑Doppler (RAD) tensors. The method derives reflector velocities from object tracks, projects them onto the line of sight, and uses a fixed analytic point‑spread function to avoid embedding sensor‑induced spread into the scene representation. This design allows accurate scene reconstruction and zero‑shot transfer to different radar configurations, achieving a 90.7% recovery of radar detections on the RADIal dataset compared to 26.9% for the best baseline.
arXiv:2609.21000v1 Announce Type: cross Abstract: Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustne...
Moving object segmentation is conventionally formulated at the sequence and object level, marking an entire instance as moving once it moves anywhere in a video. Recent instantaneous formulations dist...
arXiv:2607. 09629v1 Announce Type: cross Abstract: Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout.
The paper introduces MoRSe, a method that segments only the regions that actually move between two frames, rather than labeling entire objects as moving. Unlike prior approaches that rely on instance-level proposals and often misattribute motion to whole objects, MoRSe jointly interprets two frames with a single RGB encoder, eliminating the need for optical flow or separate mask generators. The authors also release MoRSe-5K, a benchmark of 42 sequences designed to test the model’s ability to differentiate moving and stationary regions under various camera and scene motion conditions, and demonstrate that MoRSe outperforms existing moving object segmentation and dense 4D reconstruction methods with fewer false positives.
arXiv:2609.17427v1 Announce Type: cross Abstract: Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear f...
arXiv:2610.34085v1 Announce Type: cross Abstract: Safe indoor operation requires autonomous tracking of objects that are not directly visible to the robot. Optical non-line-of-sight (NLoS) methods de...
arXiv:2609.36844v1 Announce Type: new Abstract: Transparent surfaces are ubiquitous in built environments, yet they remain a persistent failure case for robotic perception. RGB cameras perceive the b...
The paper introduces the Physics-Aware Radar Transformer (PART), a radar-only detector that predicts moving-object existence, surface points, and ground-plane velocity using Doppler-aware query initialization and physics-guided cross-attention. PART achieves high class-agnostic performance on the nuScenes dataset, excelling in rare categories and adverse conditions such as night, rain, and occlusion. The model is lightweight, with only 1.1 million parameters, and its code and pretrained weights will be released publicly.
DAPEVO is a learned visual odometry system that independently estimates image and event correspondences at shared patch locations and fuses their correlation evidence before motion refinement. It maintains image and event descriptors for each tracked patch, using a learned scalar gate to combine modality-specific correlation embeddings for each patch–frame edge, followed by a shared recurrent refinement and bundle‑adjustment update. The method supports event‑only observations and modality‑aware keyframe culling, achieving low trajectory error even when RGB frames are sparse or degraded, outperforming DPVO, RAMP‑VO, and event‑only DEVO on UZH‑FPV and TartanEvent datasets.