arXiv Computer Vision September 10, 2026 By Xianda Guo, Ruijun Zhang, Yiqun Duan, Ruilin Wang, Matteo Poggi, Keyuan Zhou, Wenzhao Zheng, Wenke Huang, Gangwei Xu, Yanlun Peng, Yuan Si, Qin Zou

ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving

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arXiv Computer Vision

Oct 21

SynCalib: Breaking Dataset Shortcuts in LiDAR-Camera Calibration via Extrinsic-Controllable Synthesis

arXiv:2610.27547v1 Announce Type: new Abstract: Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approach...

By Huihua Zhu, Shuyang Zhang, Dongqing Chen, Zhuo Yu, Jianhao Jiao, Yilong Zhu, Siyu Teng, Xiaopin Zhong, Zongze Wu

robotics multimodal benchmarks

arXiv Computer Vision

4d ago

WorkIng: A Real-World and Synthetic Highway Work-Zone Benchmark Dataset for Autonomous Navigation

WorkIng is a dual-domain benchmark for autonomous navigation in highway work zones, offering a real-world dataset (WorkIng-Highway) with 360° multi‑modal sensor data and over 12,000 annotated frames, and a synthetic counterpart (WorkIng-CARLA) that simulates 24 regulation‑aware scenarios across six topologies and traffic standards. The benchmark includes 227,399 tracked 3D bounding boxes and 100+ custom 3D assets. Experiments show that real‑world data drives performance, while scenario‑stratified synthetic augmentation improves 2D recognition, 3D detection, and trajectory planning.

By Xujun Xie, Dominik R\"o{\ss}le, Adithya Mohan, Daniel Cremers, Torsten Sch\"on

agents multimodal benchmarks

arXiv AI

Aug 11

Geometry Beats Estimated Depth: RGB-Only Multi-Camera 3D Tracking under Sim2Real

arXiv:2608. 07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth is available only for training and validation, so inference is RGB-only.

By Abdullah Naeem, Anav Katwal, Ayon Dey, Noman Khan, Md Tamjidul Hoque

fine-tuning benchmarks

arXiv Machine Learning

Jun 3

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset

arXiv:2606. 02956v1 Announce Type: cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity.

By Richard Schwarzkopf, Fabian Immel, Alexander Blumberg, Jonas Merkert, Nils Rack, Kaiwen Wang, Fabian Konstantinidis, Julian Truetsch, Carlos Fernandez, Annika B\"atz, Kevin R\"osch, Marlon Steiner, Willi Poh, Yinzhe Shen, Royden Wagner, Felix Hauser, Dominik Strutz, Jaime Villa, Gleb Stepanov, Holger Caesar, \"Omer \c{S}ahin Ta\c{s}, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller

agents computer-vision robotics multimodal benchmarks

arXiv AI

Jun 2

DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle

arXiv:2307. 06647v4 Announce Type: replace-cross Abstract: We propose DeepIPCv2, an end-to-end autonomous driving framework that integrates LiDAR-based environmental perception with command-specific control learning.

By Oskar Natan, Jun Miura

agents computer-vision robotics benchmarks

Hugging Face Trending Papers

Sep 8

DXPR: Depth-Based Vision-LiDAR Cross-Modal Place Recognition Using Vision Foundation Models

DXPR is a depth‑based cross‑modal place recognition framework that matches monocular camera queries to a LiDAR map using a single vision foundation model backbone. By converting both modalities into a unified depth image representation, DXPR learns modality‑invariant global descriptors without modality‑specific encoders. A geometry‑aware overlap miner refines pairwise metric learning by computing pixel‑level overlap scores, and extensive tests on KITTI and Boreas show strong performance across seasons, weather, and day/night conditions, outperforming prior CMPR baselines.

agents robotics multimodal safety