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Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding

Guocheng Qian, Abdullah Hamdi, Xingdi Zhang, Bernard Ghanem

TL;DR

Pix4Point addresses the challenge of applying standard Transformers to 3D point clouds by introducing PViT, a Point Vision Transformer with a Relative Progressive Tokenizer and a global-representation decoder. It further leverages image-domain pretraining through the Pix4Point pipeline, transferring weights from image-pretrained Transformers to point-cloud tasks. The approach yields substantial performance gains across 3D semantic, part, and object recognition benchmarks, underscoring the value of inductive biases and cross-modality pretraining for data-efficient 3D understanding. This work suggests a practical path toward unified, multi-modal transformers that can operate effectively across image and 3D modalities, with strong implications for scalable 3D perception systems.

Abstract

While Transformers have achieved impressive success in natural language processing and computer vision, their performance on 3D point clouds is relatively poor. This is mainly due to the limitation of Transformers: a demanding need for extensive training data. Unfortunately, in the realm of 3D point clouds, the availability of large datasets is a challenge, exacerbating the issue of training Transformers for 3D tasks. In this work, we solve the data issue of point cloud Transformers from two perspectives: (i) introducing more inductive bias to reduce the dependency of Transformers on data, and (ii) relying on cross-modality pretraining. More specifically, we first present Progressive Point Patch Embedding and present a new point cloud Transformer model namely PViT. PViT shares the same backbone as Transformer but is shown to be less hungry for data, enabling Transformer to achieve performance comparable to the state-of-the-art. Second, we formulate a simple yet effective pipeline dubbed "Pix4Point" that allows harnessing Transformers pretrained in the image domain to enhance downstream point cloud understanding. This is achieved through a modality-agnostic Transformer backbone with the help of a tokenizer and decoder specialized in the different domains. Pretrained on a large number of widely available images, significant gains of PViT are observed in the tasks of 3D point cloud classification, part segmentation, and semantic segmentation on ScanObjectNN, ShapeNetPart, and S3DIS, respectively. Our code and models are available at https://github.com/guochengqian/Pix4Point .

Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding

TL;DR

Pix4Point addresses the challenge of applying standard Transformers to 3D point clouds by introducing PViT, a Point Vision Transformer with a Relative Progressive Tokenizer and a global-representation decoder. It further leverages image-domain pretraining through the Pix4Point pipeline, transferring weights from image-pretrained Transformers to point-cloud tasks. The approach yields substantial performance gains across 3D semantic, part, and object recognition benchmarks, underscoring the value of inductive biases and cross-modality pretraining for data-efficient 3D understanding. This work suggests a practical path toward unified, multi-modal transformers that can operate effectively across image and 3D modalities, with strong implications for scalable 3D perception systems.

Abstract

While Transformers have achieved impressive success in natural language processing and computer vision, their performance on 3D point clouds is relatively poor. This is mainly due to the limitation of Transformers: a demanding need for extensive training data. Unfortunately, in the realm of 3D point clouds, the availability of large datasets is a challenge, exacerbating the issue of training Transformers for 3D tasks. In this work, we solve the data issue of point cloud Transformers from two perspectives: (i) introducing more inductive bias to reduce the dependency of Transformers on data, and (ii) relying on cross-modality pretraining. More specifically, we first present Progressive Point Patch Embedding and present a new point cloud Transformer model namely PViT. PViT shares the same backbone as Transformer but is shown to be less hungry for data, enabling Transformer to achieve performance comparable to the state-of-the-art. Second, we formulate a simple yet effective pipeline dubbed "Pix4Point" that allows harnessing Transformers pretrained in the image domain to enhance downstream point cloud understanding. This is achieved through a modality-agnostic Transformer backbone with the help of a tokenizer and decoder specialized in the different domains. Pretrained on a large number of widely available images, significant gains of PViT are observed in the tasks of 3D point cloud classification, part segmentation, and semantic segmentation on ScanObjectNN, ShapeNetPart, and S3DIS, respectively. Our code and models are available at https://github.com/guochengqian/Pix4Point .
Paper Structure (14 sections, 2 equations, 4 figures, 8 tables)

This paper contains 14 sections, 2 equations, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. PViT Network
  5. Pix4Point Pipeline
  6. Experiments and Results
  7. Training Setup
  8. 3D Semantic Segmentation
  9. 3D Part Segmentation
  10. 3D Object Classification
  11. Ablation Study and Analysis
  12. Conclusions and Future Work
  13. Additional Analysis
  14. Architecture

Figures (4)

  • Figure 1: Image-Pretrained Transformer for Point Clouds. Standard Transformers pretrained on images can be applied directly to point clouds and improve performance on a variety of 3D tasks including classification, segmentation, and part segmentation.
  • Figure 2: Pix4Point Pipeline and PViT Network. Pix4Point is composed of three stages: (1) image pretraining, (2) weight transferring, and (3) PViT downstream finetuning. PViT first projects the input point cloud into point tokens through the Relative Progressive Tokenizer $\mathbf{t}$, passes the tokens into the image-pretrained Transformer backbone $\mathbf{F}$, and then generates task outputs through the task-specific decoder $\mathbf{g}$. The parameters of $\mathbf{t}, \mathbf{F}$, and $\mathbf{g}$ are optimized jointly in the finetuning stage. Refer to sec. \ref{['sec:network']} and sec. \ref{['sec:pix4point_pipeline']} for the detailed architecture and pipeline, respectively.
  • Figure 3: Qualitative Results of PViT on S3DIS Area 5. PViT with image pretraining ($4^{th}$ column) achieves more precise segmentation results than PViT trained from scratch ($3^{rd}$ column) and Point-BERT yu2022pointbert ($2^{nd} column$).
  • Figure 4: Effect of Pretraining Strategies with and without a Frozen Backbone. We show validation curves of downstream performance of PViT on S3DIS area 5 with the same backbone (ViT-S) pretrained using different strategies: from scratch, self-supervised pretraining on ShapeNet by Point-MAE, self-supervised pretraining on ImageNet-1K by MAE (Pix4Point). Results with a frozen pretrained backbone are also included for reference. As observed, image-pretraining improves point cloud understanding more than the usual point cloud pretraining in ShapeNet.