Pointcept
/

Concerto

@@ -13,14 +13,15 @@ tags:
 This repository contains the model weights for **Concerto**, a novel approach for learning robust spatial representations presented in the paper [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://huggingface.co/papers/2510.23607).
 - **Paper:** [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://huggingface.co/papers/2510.23607)
-- **Project Page:** [https://pointcept.github.io/Concerto/](https://pointcept.github.io/Concerto/)
 - **Codebase:** [https://github.com/Pointcept/Pointcept](https://github.com/Pointcept/Pointcept)
 ## Abstract
 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.
 ## Usage
-For detailed installation, data preparation, training, and testing instructions, please refer to the [official GitHub repository](https://github.com/Pointcept/Pointcept).
 ## Citation
 If you find Concerto or the Pointcept codebase useful in your research, please cite the following papers:

 This repository contains the model weights for **Concerto**, a novel approach for learning robust spatial representations presented in the paper [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://huggingface.co/papers/2510.23607).
 - **Paper:** [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://huggingface.co/papers/2510.23607)
+- **Project Page:** [https://pointcept.github.io/Concerto/](https://pointcept.github.io/Concerto)
 - **Codebase:** [https://github.com/Pointcept/Pointcept](https://github.com/Pointcept/Pointcept)
+- **Inference:** [https://github.com/Pointcept/Concerto](https://github.com/Pointcept/Concerto)
 ## Abstract
 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.
 ## Usage
+For detailed installation, data preparation, training, and testing instructions, please refer to the [official codebase](https://github.com/Pointcept/Pointcept) and [inference demo](https://github.com/Pointcept/Concerto).
 ## Citation
 If you find Concerto or the Pointcept codebase useful in your research, please cite the following papers: