{
  "annotation_id": "7c7e1438-4598-4d73-9d5c-a748a62cf1dc",
  "date_created": "2026-02-17T05:53:15.941000Z",
  "date_modified": "2026-02-17T05:53:15.941000Z",
  "file_hash": "e5b6976a0addff6e60cc3d2aa420266b231c0e2fd6328f5e1716ef9614b7c76e",
  "private": false,
  "record": {
    "abstract": "This technical report represents the award-winning solution to the Cross-platform 3D Object Detection task in the RoboSense2025 Challenge. Our approach is built upon PVRCNN++, an efficient 3D object detection framework that effectively integrates point-based and voxel-based features. On top of this foundation, we improve cross-platform generalization by narrowing domain gaps through tailored data augmentation and a self-training strategy with pseudo-labels. These enhancements enabled our approach to secure the 3rd place in the challenge, achieving a 3D AP of 62.67% for the Car category on the phase-1 target domain, and 58.76% and 49.81% for Car and Pedestrian categories respectively on the phase-2 target domain.",
    "arxiv_id": "2601.08174",
    "authors": [
      "Xiyan Feng",
      "Wenbo Zhang",
      "Lu Zhang",
      "Yunzhi Zhuge",
      "Huchuan Lu",
      "You He"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Towards Cross-Platform Generalization: Domain Adaptive 3D Detection with Augmentation and Pseudo-Labeling",
    "url": "https://arxiv.org/abs/2601.08174",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0c725303-886e-457b-92c9-687355744f37",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}