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arXiv Computer Vision

WildDepth-v2: A Synchronized RGB--LiDAR--Radar Dataset and Benchmark for Metric Depth, Motion, and Tracking of Wildlife

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."

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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.

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arXiv Computer Vision
Sep 1

RLG-TPV: Radar- and LiDAR-Guided Tri-Perspective View Fusion for Camera-Radar 3D Object Detection

RLG-TPV introduces a multimodal Tri-Perspective View framework that fuses camera, radar, and training‑time LiDAR data for 3D object detection. It uses radar and LiDAR to guide a ray‑deformable attention lift, refining depth distributions and providing geometric supervision for side and front planes, while radar cross‑section awareness spreads evidence spatially. On nuScenes, the method attains 0.4981 mAP and 0.5959 NDS, improving orientation and velocity accuracy by about 32 % and 31 % over the CRN baseline.

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arXiv Computer Vision
Sep 18

Needles in a Raystack: Ultra-Sparse LiDAR Occupancy Detection for Bat Tracks

The paper presents a lightweight 3D U‑Net designed to detect ultra‑sparse LiDAR occupancy of bat flight paths in nocturnal field recordings. By preserving temporal resolution and combining weighted binary cross‑entropy with Dice loss, the model overcomes the class imbalance that hampers standard reconstruction methods. Experiments on real LiDAR data, cross‑checked with acoustic monitoring, show that the U‑Net successfully recovers coherent occupancy patterns along bat trajectories, offering a practical foundation for large‑scale validation, clustering of flight tracks, and integration into biodiversity‑aware turbine curtailment strategies.

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arXiv Computer Vision
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Stereo 4D Radar for 3D Object Detection: Integrating Geometric Alignment and Absolute Velocity Estimation

The paper presents a stereo 4D Radar framework for 3D object detection that uses geometric disparity between left and right radars to estimate absolute velocity and fuse complementary features. It addresses clutter, ghost reflections, and sparse data issues inherent in raw 4D Radar signals. Experiments on an in‑house dataset show significant gains, improving AP 3D by 8.82 points and AP BEV by 9.0 points over mono‑radar baselines.

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DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes

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.

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arXiv Computer Vision
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DeepForestVisionV2: Ecology-Driven Taxonomy Expansion for Camera-Trap Monitoring in African Tropical Forests

arXiv:2606.20223v2 Announce Type: replace Abstract: Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Am...

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