Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling (2111.14819v2)

Abstract: We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a point cloud into several local point patches, and a point cloud Tokenizer with a discrete Variational AutoEncoder (dVAE) is designed to generate discrete point tokens containing meaningful local information. Then, we randomly mask out some patches of input point clouds and feed them into the backbone Transformers. The pre-training objective is to recover the original point tokens at the masked locations under the supervision of point tokens obtained by the Tokenizer. Extensive experiments demonstrate that the proposed BERT-style pre-training strategy significantly improves the performance of standard point cloud Transformers. Equipped with our pre-training strategy, we show that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy on the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made designs. We also demonstrate that the representations learned by Point-BERT transfer well to new tasks and domains, where our models largely advance the state-of-the-art of few-shot point cloud classification task. The code and pre-trained models are available at https://github.com/lulutang0608/Point-BERT

Overview of Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

The paper introduces Point-BERT, a novel framework designed to extend the BERT (Bidirectional Encoder Representations from Transformers) pre-training methodology to 3D point cloud data using Transformers. The primary innovation lies in developing a Masked Point Modeling (MPM) task, analogous to the Masked LLMing (MLM) task used in BERT, enabling more effective representation learning for 3D point cloud data.

Key Contributions

1. Point Tokenization via dVAE:
   • The authors propose a discrete variational autoencoder (dVAE) to tokenize 3D point clouds. This tokenizer generates discrete point tokens by learning meaningful local geometric patterns within the point cloud data.
   • The process involves dividing a point cloud into several patches, encoding these patches into embeddings, and converting these embeddings into discrete tokens that serve as a foundational layer for the MPM task.
2. Masked Point Modeling (MPM):
   • MPM is devised as the pre-training objective where some point patches are masked and the model is trained to predict these masked portions based on the context provided by the unmasked patches.
   • This strategy facilitates capturing structural and semantic patterns inherent to 3D point clouds, improving downstream task performance significantly.
3. Transformer-based Architecture:
   • A standard Transformer architecture is applied on the point cloud tokens with minimal inductive biases, unlike prior works which often relied on local aggregation or neighbor embedding techniques.
   • Point-BERT aligns more closely with the successes witnessed in NLP and image domains, emphasizing scalability and generalization across tasks.

Experimental Results

Point-BERT demonstrates strong empirical results across multiple benchmarks, highlighting its efficacy:

• Classification Accuracy: Achieves 93.8% on ModelNet40 and 83.1% on ScanObjectNN, indicating robust performance on both synthetic and real-world datasets.
• Few-shot Learning: Exhibits significant improvements in few-shot scenarios, showcasing its ability to generalize from limited labeled data.
• Part Segmentation: Outperforms baseline models in segmenting parts in the ShapeNetPart dataset, demonstrating its applicability in intricate tasks requiring fine-grained understanding.
• Transfer Learning: Effectively transfers learned representations to real-world datasets like ScanObjectNN, suggesting its practical utility in diverse settings.

Implications and Future Directions

The implications of this paper extend both practically and theoretically:

• Unified Model for 3D Representation: By reducing inductive biases, Point-BERT potentially standardizes the approach to 3D representation learning, fostering unified modeling between 2D and 3D data.
• Scalability and Data Efficiency: The use of a BERT-style pre-training paradigm is promising for situations with limited annotated 3D data, promoting better scalability and efficiency when obtaining labeled data is challenging.

Future investigations might explore optimizing the computational overhead associated with Transformer-based models, potentially making them more feasible for broader industrial applications. Additionally, diversifying the range of tasks and datasets to further validate the versatility and robustness of Point-BERT would be beneficial.

In conclusion, Point-BERT provides a compelling framework that harnesses the strengths of Transformer architectures and BERT-style pre-training, significantly enhancing 3D point cloud representation learning. This advancement hints at broader capabilities and applications of Transformers beyond traditional domains, promising an exciting trajectory for future research and development in 3D vision.