Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
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Original Paper
The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited 3D deep learning, mainly due to the limited availability of large-scale 3D datasets. Merging multiple available data sources and letting them collaboratively train a single model is a potential solution. However, due to the large domain gap between 3D point cloud datasets, such mixed supervision could adversely affect the model's performance and lead to degenerated performance (i.e., negative transfer) compared to single-dataset training. In view of this challenge, we introduce Point Prompt Training (PPT), a novel framework for multi-dataset synergistic learning in the context of 3D representation learning that supports multiple pre-training paradigms. Based on this framework, we propose Prompt-driven Normalization, which adapts the model to different datasets with domain-specific prompts and Language-guided Categorical Alignment that decently unifies the multiple-dataset label spaces by leveraging the relationship between label text. Extensive experiments verify that PPT can overcome the negative transfer associated with synergistic learning and produce generalizable representations. Notably, it achieves state-of-the-art performance on each dataset using a single weight-shared model with supervised multi-dataset training. Moreover, when served as a pre-training framework, it outperforms other pre-training approaches regarding representation quality and attains remarkable state-of-the-art performance across over ten diverse downstream tasks spanning both indoor and outdoor 3D scenarios.

Summary Notes

Unlocking 3D Deep Learning with Multi-dataset Point Prompt Training

The field of deep learning has seen remarkable progress, especially with the advent of large datasets in image analysis and natural language processing.
However, 3D deep learning presents unique challenges due to smaller dataset sizes and the complexities of processing 3D point cloud data.
These issues have slowed advancements, and the usual single-dataset training methods often fall short, leading to what's called negative transfer when models encounter data from various sources.
Enter Point Prompt Training (PPT), a groundbreaking framework designed to enable multi-dataset synergistic training. Let's explore how PPT works and the potential it holds for the future of 3D representation learning.

The Promise of Multi-dataset Synergistic Training

PPT aims to leverage multiple datasets simultaneously, allowing a model to perform well across diverse data landscapes by minimizing overall loss.
The main challenges here are the differences in data distribution among datasets that can cause negative transfer. PPT tackles these with two key innovations:
  • Domain Prompt Adapter: Adjusts the model for specific datasets using domain-specific prompts, customizing the focus and processing abilities for each dataset.
  • Categorical Alignment: Ensures consistent training across different datasets by aligning categories through textual relationships using a pre-trained text encoder, thus connecting varied data sources.

How Point Prompt Training Works

PPT introduces domain-specific prompts during training to tailor the model's focus per dataset, enhancing dataset-sensitive learning.
Alongside, Prompt-driven Normalization (PDNorm) uses these prompts to adapt normalization layers in the network, embedding dataset-specific characteristics into the model's operations.
Language-guided Categorical Alignment (LCA) further aligns point representations with category-specific language embeddings, ensuring consistency across datasets and reducing negative transfer, improving the model's generalization abilities.

Proving PPT's Effectiveness

Extensive testing confirms PPT's capability to handle negative transfer and improve performance across numerous 3D datasets, like ScanNet and S3DIS.
It has achieved top-notch performance in over ten different downstream tasks, showcasing superior representation quality over other pre-training methods.

Looking Forward

PPT marks a major advancement in 3D deep learning, offering a scalable, efficient approach to learning across multiple datasets.
It addresses negative transfer effectively and boosts model performance, opening new research and application possibilities in 3D scene understanding and more.
The future looks promising for further developments in prompt-learning techniques and applying this framework to other 3D data types.
PPT brings us closer to fully unlocking the potential of 3D deep learning, offering AI engineers and researchers powerful new tools to innovate and explore the vast possibilities of 3D data analysis and interpretation.

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