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ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding (2212.05171v4)

Published 10 Dec 2022 in cs.CV

Abstract: The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such as language. Inspired by this, leveraging multimodal information for 3D modality could be promising to improve 3D understanding under the restricted data regime, but this line of research is not well studied. Therefore, we introduce ULIP to learn a unified representation of images, texts, and 3D point clouds by pre-training with object triplets from the three modalities. To overcome the shortage of training triplets, ULIP leverages a pre-trained vision-LLM that has already learned a common visual and textual space by training with massive image-text pairs. Then, ULIP learns a 3D representation space aligned with the common image-text space, using a small number of automatically synthesized triplets. ULIP is agnostic to 3D backbone networks and can easily be integrated into any 3D architecture. Experiments show that ULIP effectively improves the performance of multiple recent 3D backbones by simply pre-training them on ShapeNet55 using our framework, achieving state-of-the-art performance in both standard 3D classification and zero-shot 3D classification on ModelNet40 and ScanObjectNN. ULIP also improves the performance of PointMLP by around 3% in 3D classification on ScanObjectNN, and outperforms PointCLIP by 28.8% on top-1 accuracy for zero-shot 3D classification on ModelNet40. Our code and pre-trained models are released at https://github.com/salesforce/ULIP.

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Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

The paper "ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding" addresses a critical challenge in 3D visual recognition: the limitations posed by datasets with insufficient annotated data and restricted category sets. Traditionally, the 2D domain has benefited significantly from integrating multimodal sources, such as language, to ameliorate similar limitations. The authors propose leveraging this approach to improve 3D understanding by introducing ULIP, a framework that learns a unified representation across images, texts, and 3D point clouds through pre-training. This endeavor seeks to enhance performance metrics in 3D classification tasks, particularly when data is scarce.

Core Contributions

Unified Triplet Learning:

ULIP employs a novel mechanism of unified representation learning through the formulation of object triplets consisting of image, text, and point cloud data. This design facilitates the alignment of 3D representations with vision-LLMs, specifically employing a CLIP-based model, which has established a common visual-textual space through extensive image-text pair training.

Adaptation Across Modalities:

The method demonstrates flexibility by being agnostic to 3D backbone network architectures. Thus, ULIP can seamlessly integrate with various existing architectures, enhancing their performance upon pre-training. This is evidenced when ULIP is applied to multiple backbones, including PointNet++, PointMLP, and PointBERT.

Performance Metrics:

ULIP achieves state-of-the-art results in standard and zero-shot 3D classification tasks. Notably, it raises the top-1 accuracy in zero-shot 3D classification on ModelNet40 by 28.8% compared to PointCLIP. Similarly, PointMLP sees approximately a 3% improvement in 3D classification on ScanObjectNN, marking significant advancements in accuracy metrics for both datasets.

Implications and Future Prospects

Theoretical Enhancements:

The integration of multimodal features into the 3D domain opens substantial avenues for research, especially in scenarios with limited data. It suggests a robust framework where cross-modal knowledge transfer can be pivotal for advancing 3D understanding.

Practical Applications:

These improvements have important implications for real-world applications such as augmented reality, autonomous driving, and robotics, where 3D visual recognition plays a crucial role. The ability to enhance model accuracy with limited 3D data collection lowers the practical barriers by relying on cross-modal pre-trained information.

Cross-Modal and Retrieval Potential:

ULIP enables not just improved recognition but also facilitates various cross-modal applications. An exciting application is the image-to-point cloud retrieval task, which underscores the potential for interactions between modalities, emphasizing the versatile applicability of ULIP.

Future Developments in AI

The paper signals a growing trend towards harnessing multifaceted data sources to enrich the learning capacity of AI models. Future research could explore expanding ULIP to other modalities or enhancing the scale and diversity of pre-trained datasets further. The extensive leveraging of existing knowledge across domains is likely to continue as the community seeks to push the boundaries of machine comprehension in complex environments.

Overall, ULIP stands as a testament to the promise of multimodal integration in overcoming existing limitations in 3D visual recognition. Its robust numerical results point towards a thoughtful advancement in AI, potentially setting a foundation for further cross-domain innovations and applications.

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References (60)
  1. 3d semantic parsing of large-scale indoor spaces. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1534–1543, 2016.
  2. Multi-modal auto-encoders as joint estimators for robotics scene understanding. In Robotics: Science and systems, volume 5, 2016.
  3. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.
  4. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104–120. Springer, 2020.
  5. Multimodal semi-supervised learning for 3d objects. arXiv preprint arXiv:2110.11601, 2021.
  6. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In European conference on computer vision, pages 628–644. Springer, 2016.
  7. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
  8. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  9. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop, pages 178–178. IEEE, 2004.
  10. Open vocabulary object detection with pseudo bounding-box labels. arXiv preprint arXiv:2111.09452, 2021.
  11. Revisiting point cloud shape classification with a simple and effective baseline. In International Conference on Machine Learning, pages 3809–3820. PMLR, 2021.
  12. 3d semantic segmentation with submanifold sparse convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 9224–9232, 2018.
  13. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921, 2021.
  14. Pct: Point cloud transformer. Computational Visual Media, 7(2):187–199, 2021.
  15. Mvtn: Multi-view transformation network for 3d shape recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1–11, 2021.
  16. Randla-net: Efficient semantic segmentation of large-scale point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11108–11117, 2020.
  17. Large-scale point cloud semantic segmentation with superpoint graphs. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4558–4567, 2018.
  18. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694–9705, 2021.
  19. Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019.
  20. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965–10975, 2022.
  21. Oscar: Object-semantics aligned pre-training for vision-language tasks. In European Conference on Computer Vision, pages 121–137. Springer, 2020.
  22. Pointcnn: Convolution on X-transformed points. arXiv preprint arXiv:1801.07791, 2018.
  23. Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17182–17191, 2022.
  24. Lidar r-cnn: An efficient and universal 3d object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7546–7555, 2021.
  25. Densepoint: Learning densely contextual representation for efficient point cloud processing. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5239–5248, 2019.
  26. Relation-shape convolutional neural network for point cloud analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8895–8904, 2019.
  27. Group-free 3d object detection via transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2949–2958, 2021.
  28. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019.
  29. Rethinking network design and local geometry in point cloud: A simple residual mlp framework. arXiv preprint arXiv:2202.07123, 2022.
  30. Voxnet: A 3d convolutional neural network for real-time object recognition. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 922–928. IEEE, 2015.
  31. An end-to-end transformer model for 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2906–2917, 2021.
  32. Slip: Self-supervision meets language-image pre-training. In European Conference on Computer Vision, pages 529–544. Springer, 2022.
  33. Masked autoencoders for point cloud self-supervised learning. arXiv preprint arXiv:2203.06604, 2022.
  34. Styleclip: Text-driven manipulation of stylegan imagery. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2085–2094, 2021.
  35. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652–660, 2017.
  36. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30, 2017.
  37. Pointnext: Revisiting pointnet++ with improved training and scaling strategies. arXiv preprint arXiv:2206.04670, 2022.
  38. Geometric back-projection network for point cloud classification. IEEE Transactions on Multimedia, 24:1943–1955, 2021.
  39. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR, 2021.
  40. Surface representation for point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18942–18952, 2022.
  41. Learning inner-group relations on point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15477–15487, 2021.
  42. Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10529–10538, 2020.
  43. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490, 2019.
  44. Kpconv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6411–6420, 2019.
  45. Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019.
  46. Softgroup for 3d instance segmentation on point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2708–2717, 2022.
  47. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1–12, 2019.
  48. Monocular 3d scene understanding with explicit occlusion reasoning. In CVPR 2011, pages 1993–2000. IEEE, 2011.
  49. Dgcnn: Disordered graph convolutional neural network based on the gaussian mixture model. Neurocomputing, 321:346–356, 2018.
  50. Pointconv: Deep convolutional networks on 3d point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9621–9630, 2019.
  51. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912–1920, 2015.
  52. Walk in the cloud: Learning curves for point clouds shape analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 915–924, 2021.
  53. Adaptive cross-modal few-shot learning. Advances in Neural Information Processing Systems, 32, 2019.
  54. Learning geometry-disentangled representation for complementary understanding of 3d object point cloud. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3056–3064, 2021.
  55. Spidercnn: Deep learning on point sets with parameterized convolutional filters. In Proceedings of the European Conference on Computer Vision (ECCV), pages 87–102, 2018.
  56. Let images give you more: Point cloud cross-modal training for shape analysis. arXiv preprint arXiv:2210.04208, 2022.
  57. Center-based 3d object detection and tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11784–11793, 2021.
  58. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19313–19322, 2022.
  59. Pointclip: Point cloud understanding by clip. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8552–8562, 2022.
  60. Point transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16259–16268, 2021.
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Authors (9)
  1. Le Xue (23 papers)
  2. Mingfei Gao (26 papers)
  3. Chen Xing (31 papers)
  4. Roberto Martín-Martín (79 papers)
  5. Jiajun Wu (249 papers)
  6. Caiming Xiong (337 papers)
  7. Ran Xu (89 papers)
  8. Juan Carlos Niebles (95 papers)
  9. Silvio Savarese (200 papers)
Citations (138)
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GitHub

  1. GitHub - salesforce/ULIP (445 stars)