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arXiv Computer Vision By Muhammad Aamir, Sangyun Shin, Naoya Muramatsu, Matthew Wijers, Amir Patel, Andrew Loveridge, Andrew Markham

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

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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."

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.

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