Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations

yujia, Xiaoyang Wu, Yixing Lao, Wang Chengyao, Zhuotao Tian, Naiyan Wang, Hengshuang Zhao

Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations: 164 upvotes on Hugging Face Daily Papers, #1 of 36 papers on 2025-10-28. Day-by-day upvote history. It lost 17 votes when the Hub removed votes in bulk.

Humans learn abstract concepts through multisensory synergy, and once formed, such representations can often be recalled from a single modality. Inspired by this principle, we introduce Concerto, a minimalist simulation of human concept learning for spatial cognition, combining 3D intra-modal self-distillation with 2D-3D cross-modal joint embedding. Despite its simplicity, Concerto learns more coherent and informative spatial features, as demonstrated by zero-shot visualizations. It outperforms both standalone SOTA 2D and 3D self-supervised models by 14.2% and 4.8%, respectively, as well as their feature concatenation, in linear probing for 3D scene perception. With full fine-tuning, Concerto sets new SOTA results across multiple scene understanding benchmarks (e.g., 80.7% mIoU on ScanNet). We further present a variant of Concerto tailored for video-lifted point cloud spatial understanding, and a translator that linearly projects Concerto representations into CLIP's language space, enabling open-world perception. These results highlight that Concerto emerges spatial representations with superior fine-grained geometric and semantic consistency.

Paper page on Hugging Face · arXiv

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