Source-linked AI summary
PointGPT: Auto-regressively Generative Pre-training from Point Clouds
Guangyan Chen, Meiling Wang, Yi Yang, Kai Yu, Li Yuan, Yufeng Yue
TL;DR
Point clouds lack the inherent order and information characteristics that make GPT-style autoregressive learning effective, while current 3D methods often require costly fully supervised training. PointGPT orders point patches and autoregressively predicts them with dual masking and an extractor-generator decoder, achieving state-of-the-art results across downstream tasks, including 94.9% on ModelNet40 and 93.4% on ScanObjectNN.
Problem
Point clouds lack inherent order, have heavy redundancy, and create a gap between point-level generation and downstream semantic tasks, while current 3D approaches typically require fully supervised training.
Method
PointGPT partitions point clouds into patches, orders them with the Morton curve, and uses a dual-masked extractor-generator transformer decoder to predict subsequent patches autoregressively.
Results
PointGPT achieves state-of-the-art performance across downstream tasks, including 94.9% accuracy on ModelNet40, 93.4% on ScanObjectNN, and new state-of-the-art results on all four few-shot benchmarks.
Takeaways & Limitations
PointGPT provides a single-modal point-cloud self-supervised framework whose scaled models generalize across downstream tasks without cross-modal information or teacher models.
Abstract
from arXiv · showhide
Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, addressing the challenges associated with disorder properties, low information density, and task gaps. Specifically, a point cloud auto-regressive generation task is proposed to pre-train transformer models. Our method partitions the input point cloud into multiple point patches and arranges them in an ordered sequence based on their spatial proximity. Then, an extractor-generator based transformer decoder, with a dual masking strategy, learns latent representations conditioned on the preceding point patches, aiming to predict the next one in an auto-regressive manner. Our scalable approach allows for learning high-capacity models that generalize well, achieving state-of-the-art performance on various downstream tasks. In particular, our approach achieves classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the ScanObjectNN dataset, outperforming all other transformer models. Furthermore, our method also attains new state-of-the-art accuracies on all four few-shot learning benchmarks.
1 Introduction
PointGPT adapts GPT-style self-supervised learning to point clouds by ordering point patches and predicting them autoregressively. Its dual masking, extractor-generator decoder, and scaling strategy target generalization and high-capacity downstream performance.
- Motivation: Point clouds lack inherent order, contain heavy redundancy, and present a gap between point-level generation and downstream semantic tasks.These properties complicate direct application of GPT-style autoregressive learning.
- Method: A dual masking strategy reduces redundancy and creates a more challenging generation task requiring comprehensive understanding.The approach additionally masks attending tokens for each token.
- Method: PointGPT partitions point clouds into irregular patches, orders them with the Morton curve, and predicts subsequent patches autoregressively.The method uses an extractor-generator transformer decoder conditioned on unmasked preceding contents.
- Contributions: PointGPT avoids positional information leakage associated with reconstruction-region specifications and surpasses comparable single-modal self-supervised methods.The paper attributes improved generalization ability to this design.
- Results: 94.9% ModelNet40 accuracy and 93.4% ScanObjectNN accuracy outperform all other transformer models.The scaled models also achieve new state-of-the-art performance on all four few-shot learning benchmarks.
- Scaling: Larger mixed pre-training datasets and a post-pre-training stage facilitate high-capacity models that achieve state-of-the-art performance across downstream tasks.The post-pre-training stage uses supervised learning on collected labeled data to incorporate semantic information from multiple sources.
2 Related Work
Prior point-cloud self-supervised learning work builds on methods developed for NLP and image processing, especially contrastive representation learning. Existing approaches include instance discrimination and cross-modal or cross-view invariance constraints.
- Self-supervised learning: Self-supervised learning designs pretext tasks that learn data distributions and produce features for subsequent supervised modeling without labeled data.Contrastive learning groups similar samples closer while separating diverse samples.
- Point-cloud methods: Point-cloud self-supervised research has extensively investigated contrastive methods for representation learning.These methods adapt self-supervised objectives from NLP and image processing to point-cloud data.
- Point-cloud methods: DepthContrast constructs augmented depth maps and performs instance discrimination on extracted global features.This provides one contrastive strategy for point-cloud representation learning.
- Point-cloud methods: MVIF uses cross-modal and cross-view invariance constraints to learn self-supervised representations.Its objective addresses consistency across modalities and viewpoints.
3 PointGPT
PointGPT pre-trains point-cloud transformers with autoregressive point-patch prediction, addressing unordered structure, redundancy, and the gap between generation and downstream understanding. It sequences patches geometrically, uses extractor-generator decoding with dual masking, and applies the learned extractor to downstream tasks.
- Transformer Decoder with a Dual Masking Strategy: The transformer decoder autoregressively predicts subsequent point patches from latent representations conditioned on unmasked preceding tokens.The extractor uses dual masking, while the generator predicts the next patches in coordinate space.
- Point Cloud Sequencer: PointGPT divides point clouds into irregular patches, orders them with Morton codes, and embeds them into geometric tokens.Farthest point sampling and K-nearest neighbors construct patches; PointNet extracts patch information from normalized coordinates.
- Generation Target: The generation target predicts coordinates for subsequent patches using the sum of L1- and L2-form Chamfer-distance losses.Ground-truth targets are the last n′ patches among the sorted point patches.
- Downstream Fine-tuning: After pre-training, the generator is discarded and the extractor supplies learned representations for downstream tasks without dual masking.During fine-tuning, generation loss can also be combined with downstream loss as Lf = Ld + λ × Lg.
- Downstream Fine-tuning: Intermediate fine-tuning uses a labeled hybrid dataset to reduce potential overfitting from directly fine-tuning on limited target-dataset supervision.The hybrid dataset collects and aligns multiple point-cloud datasets.
4 Experiments
PointGPT is evaluated across classification, few-shot learning, part segmentation, and ablation studies using models with different capacities and training stages. The experiments show strong downstream performance and identify design choices that improve representations while introducing computational trade-offs.
- Experimental Setup: PointGPT is evaluated on object classification, few-shot learning, and part segmentation using PointGPT-S, PointGPT-B, and PointGPT-L.PointGPT-S uses ShapeNet pre-training without post-pre-training, whereas PointGPT-B and PointGPT-L use both pre-training and post-pre-training on collected hybrid datasets.
- Object Classification: PointGPT-S outperforms other single-modal self-supervised methods on both ScanObjectNN and ModelNet40 classification, while PointGPT-L surpasses competing methods.ScanObjectNN evaluates OBJ-BG, OBJ-ONLY, and PB-T50-RS settings; ModelNet40 evaluation uses standard voting and coordinate-only inputs.
- Few-shot Learning: PointGPT outperforms other methods in all four ModelNet40 few-shot tests, particularly in the 10-shot settings.The tests use 5-way or 10-way classification with 10-shot or 20-shot training, averaged over 10 independent trials.
- Part Segmentation: PointGPT-L achieves superior part-segmentation performance compared with all other methods on ShapeNetPart.The evaluation reports mean intersection over union across classes and instances.
- Ablation Studies: A generator depth of 4 is selected because deeper generators improve performance but increase computational complexity.The ablation attributes stronger semantic representations to the extractor-generator architecture, especially with a deep generator.
- Ablation Studies: Two-stage generation targets outperform point-coordinate targets, while combining l1- and l2-form Chamfer-distance losses achieves superior performance.Handcrafted FPFH features underperform, and teacher-network targets add pre-training and inference costs.
- Ablation Studies: Relative direction prompts outperform absolute positional encoding and no positional encoding, while excessive or insufficient dual masking reduces performance.The best fine-tuning generation-loss coefficient is λ = 3; the authors hypothesize that relative prompts reduce overfitting to patch order.
5 Conclusion
PointGPT extends GPT-style self-supervised learning to point clouds and addresses their disorder, information-density differences, and generation-to-downstream-task gaps. Its strong generalization and scaled models achieve state-of-the-art performance across diverse downstream tasks, while remaining much smaller in data and model scale than NLP and image-processing systems.
- PointGPT extends the GPT concept to point clouds while addressing disorder, information-density differences, and gaps between generation and downstream tasks.
- The method avoids overall object shape leakage compared with recent masked point modeling approaches, improving generalization ability.
- PointGPT's effectiveness and strong generalization are verified across various tasks, outperforming single-modal methods with similar model capacities.
- Scaled PointGPT models achieve state-of-the-art performance on various downstream tasks without cross-modal information or teacher models.
- PointGPT's explored data and model scales remain several orders of magnitude smaller than those in NLP and image-processing domains.