SV2V RSim: A Comprehensive Benchmark for Self Selective V2V Cooperative Perception with Near Realistic Data Table of Contents: 1. Highlights 2. News 3. Getting Started 4. Benchmark 5. TODO List 6. Contaction Highlights 1. The SV2V RSim dataset spans four maps, incorporates four distinct weather conditions and six time periods from sunrise to night, and comprises 203K LiDAR frames, 402K RGB frames, and 788K annotated 3D bounding boxes across 17 object classes. These intricate environments, marked by a high density of road users and constantly evolving traffic patterns, offer a new benchmark dataset for advancing V2V cooperative perception research. 2. Compared with other synthetic datasets, SV2V RSim provides more realistic rendering quality and more accurate asset geometry. 3. SV2V RSim supports multiple downstream tasks, including collaborative object detection, depth estimation, semantic segmentation, and more. News [2026.05] 🔥 SV2V RSim dataset is availale Getting Started Data Format For SV2V RSim Dataset ├── train training dataset ├── {scene name} The scene name folder contains all agents that have interacted with the ego vehicle. ├── {agent id} The agent id folder contains th…
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