M2S uses multi-level feature enhancement, auxiliary point cloud reconstruction, and multi-teacher contrastive distillation to boost ego-only 3D mAP by up to 8.64% on V2XSet, V2V4Real, and DAIR-V2X when applied to CoSDH and other detectors.
Advances in Neural Information Processing Systems34, 29541–29552 (2021)
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
CooperScene provides 59K synchronized frames with 344K 3D annotations from multi-modal sensors on 3 CAVs and 1 RSU plus real C-V2X communication traces for cooperative autonomy benchmarking.
VQSOP applies sparsity-exploiting vector quantization and a dual-branch refinement module to cut communication volume by up to 82x while claiming state-of-the-art 3D occupancy prediction performance.
citing papers explorer
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C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation
M2S uses multi-level feature enhancement, auxiliary point cloud reconstruction, and multi-teacher contrastive distillation to boost ego-only 3D mAP by up to 8.64% on V2XSet, V2V4Real, and DAIR-V2X when applied to CoSDH and other detectors.
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CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization
CooperScene provides 59K synchronized frames with 344K 3D annotations from multi-modal sensors on 3 CAVs and 1 RSU plus real C-V2X communication traces for cooperative autonomy benchmarking.
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Sparse-Aware Vector Quantization for Bandwidth-Efficient Collaborative 3D Semantic Occupancy Prediction
VQSOP applies sparsity-exploiting vector quantization and a dual-branch refinement module to cut communication volume by up to 82x while claiming state-of-the-art 3D occupancy prediction performance.