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ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding

Le Xue, Ning Yu, Shu Zhang, Artemis Panagopoulou, Junnan Li, Roberto Martín-Martín, Jiajun Wu, Caiming Xiong, Ran Xu, Juan Carlos Niebles, Silvio Savarese

arXiv:2305.08275v4cs.CV

TL;DR

Existing multimodal 3D frameworks struggle to obtain scalable, diverse language descriptions without costly manual annotation. ULIP-2 uses large multimodal models to generate holistic descriptions from rendered views using only 3D data, then aligns point clouds, images, and text; it improves downstream performance and releases large tri-modal datasets. Its pre-training is primarily evaluated on object-level 3D shape datasets, leaving scene-level understanding for future work.

  • Problem

    Existing frameworks lack scalable, diverse language descriptions for 3D shapes and commonly depend on human annotations during multimodal data collection.

  • Method

    ULIP-2 renders 3D shapes from holistic viewpoints, uses large multimodal models to generate descriptions, and aligns point clouds, images, and text using scaled multimodal pre-training.

  • Results

    ULIP-2 improves all reported downstream tasks, including 50.6% top-1 accuracy on Objaverse-LVIS, 84.7% on ModelNet40, and 91.5% ScanObjectNN accuracy with 1.4 million parameters.

  • Takeaways & Limitations

    ULIP-2 supports scalable multimodal 3D representation learning without human annotations and provides released Objaverse and ShapeNet tri-modal datasets.

  • Takeaways & Limitations

    ULIP-2’s pre-training primarily uses object-level 3D shape datasets, whose distribution and complexity differ from scene-level 3D data.

Abstract

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Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing frameworks to curate such multimodal data, in particular language descriptions for 3D shapes, are not scalable, and the collected language descriptions are not diverse. To address this, we introduce ULIP-2, a simple yet effective tri-modal pre-training framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input, eliminating the need for any manual 3D annotations, and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multimodal representation learning. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification, standard 3D classification with fine-tuning, and 3D captioning (3D-to-language generation). It achieves a new SOTA of 50.6% (top-1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning, ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://github.com/salesforce/ULIP.

1. Introduction

ULIP-2 addresses the scalability and quality bottleneck in language data for multimodal 3D learning by generating holistic descriptions automatically from rendered views using only 3D data. It aligns point clouds, images, and text, and reports improvements across downstream tasks while releasing large tri-modal datasets.

  • Method: ULIP-2 aligns holistic text, rendered images, and 3D point clouds through a pre-aligned and frozen vision-language feature space.After pre-training, the learned 3D encoder supports downstream tasks.
  • Motivation: Existing multimodal 3D frameworks rely on costly human annotations, metadata, or short category descriptions that limit scalable, diverse language-data collection.The paper identifies language as a critical bottleneck for assembling scalable, high-quality, well-aligned multimodal 3D data.
  • Method: ULIP-2 renders each 3D shape from holistic viewpoints and uses a large multimodal model to generate detailed descriptions for the resulting images.The process creates language data from the 3D shape itself rather than requiring manual 3D annotations.
  • Results: 50.6% top-1 accuracy on Objaverse-LVIS surpasses OpenShape by 3.8%, while ModelNet40 zero-shot classification reaches 84.7%.These are reported as zero-shot classification results.
  • Results: 91.5% overall accuracy on ScanObjectNN is achieved with a compact model containing only 1.4 million parameters.The paper also demonstrates 3D-to-language generation capabilities and improvements across downstream tasks.
  • Resources: ULIP-2 releases ULIP-Objaverse and ULIP-ShapeNet, large-scale tri-modal datasets containing point clouds, images, and language descriptions.The datasets are intended to support further multimodal 3D research.

2. Related Work

Related work spans two-modal image-language learning, emerging tri-modal 3D representation learning, generative multimodal annotation, and point-cloud encoders. ULIP-2 builds on these directions while targeting language-data scalability and richer descriptions.

  • Multimodal Representation Learning: Image-language methods either model region-token interactions with expensive Transformers or independently encode images and text before aligning their features.CLIP and SLIP exemplify the simpler feature-alignment approach.
  • Multimodal Representation Learning: Prior 3D methods still face scalability limits from metadata and category-name dependencies, while their prompt-based pseudo-captions lack fine-grained detail and variation.A concurrent extension improves performance but retains manual annotation and complicated data engineering requirements.
  • Generative Large Multimodal Models: ULIP-2 uses BLIP-2 to generate diverse annotations for 3D shapes and support richer multimodal 3D representations.The paper also studies the caption-generation choices in an ablation.
  • 3D Point Cloud Understanding: ULIP-2 employs Point-BERT and PointNeXt as 3D encoders to leverage their point-cloud understanding capabilities.These encoders represent the point-cloud modeling component of the framework.

3. Method

ULIP-2 combines scalable triplet creation from 3D data with efficient tri-modal pre-training that aligns point clouds, rendered images, and generated descriptions. It uses contrastive alignment and scaled multimodal backbones, yielding stronger zero-shot classification than prior approaches.

  • 3.1. Scalable Triplet Creation: ULIP-2 creates comprehensive 3D–image–language triplets without human annotations, using only 3D data and enabling application to unannotated datasets.Its language descriptions are generated from rendered views rather than dataset metadata.
  • 3.1. Scalable Triplet Creation: For each 3D shape, the method extracts a point cloud, renders images from multiple viewpoints, and uses BLIP-2 to generate descriptive texts for the images.Generated sentences are ranked using CLIP similarities before forming the multimodal data.
  • 3.3. Tri-modal Pre-training: The 3D encoder is trained to align point-cloud features with frozen, pre-aligned OpenCLIP image and text features in a unified feature space.The target encoders use the largest OpenCLIP ViT-G/14 version in most experiments.
  • 3.3. Tri-modal Pre-training: The objective combines 3D-to-image and 3D-to-text contrastive losses, making matched multimodal features stand out from features belonging to other samples.The contrastive formulation uses a learnable temperature parameter and bidirectional matching terms.

4. Experiments

ULIP-2 constructs scalable tri-modal datasets from 3D shapes and evaluates them across zero-shot classification, standard classification, and 3D-to-language generation.

  • 4.1. ULIP-Objaverse Triplets and ULIP-ShapeNet Triplets Creation: Objaverse triplets use 12 rendered views and ten independently generated BLIP-2 descriptions per view, ranked with CLIP similarity.ShapeNet triplets use 30 equally spaced views with RGB and depth images, applying the same description-generation method.
  • 4.2. Downstream Tasks: ULIP-2 is benchmarked on ModelNet40, Objaverse-LVIS, and ScanObjectNN across three downstream tasks.The tasks are zero-shot 3D classification, standard single-modality 3D classification, and 3D-to-language generation evaluated with CIDEr.
  • 4.3. Comparisons to Baselines: 91.5% overall accuracy on ScanObjectNN sets a new record with the PointNeXt backbone and only 1.4 million parameters.ULIP-2 also reports a 4.0% gain over training from scratch with PointNeXt.
  • 4.3. Comparisons to Baselines: ULIP-2 improves 3D captioning CIDEr by 28.3% when its pre-trained encoder is integrated with a frozen LLM through X-InstructBLIP.The generated captions are described as more accurate and descriptive than those from the comparison model.

5. Ablation Study

Ablations show that generated holistic-view captions, stronger multimodal models, and backbone scaling each contribute to ULIP-2’s performance, with gains diminishing beyond a sufficiently large 3D backbone.

  • 5.1. Ablation on the effect of the generated captions: Generated BLIP-2 captions significantly improve ModelNet40 zero-shot classification compared with ULIP’s manual descriptions.The ablation changes only the language modality while keeping the remaining ULIP settings aligned.
  • 5.2. Ablation on the effect of different large multimodal models: BLIP-2-generated descriptions outperform BLIP-generated descriptions in ModelNet40 zero-shot classification with a Point-BERT backbone pre-trained on ShapeNet.The authors attribute the difference to BLIP-2’s evolved image-understanding capability.
  • 5.3. Ablation on the effect of the number of holistic views: Increasing the number of holistic views increases zero-shot classification accuracy, supporting the benefit of diverse view-based descriptions.The ablation uses the top-1 BLIP-2 caption for each selected view.
  • 5.4. Ablation on the effect of top-k captions: The top-1 CLIP-score-ranked caption performs best among different top-k selections of ten independently generated BLIP-2 captions.The authors describe the top-1 caption as more noise-proof for multimodal learning.
  • 5.5. Scaling Up the Backbone Models: Larger CLIP models improve results, while 3D-backbone performance peaks around 32.5M parameters before gains diminish.The selected configuration balances performance and model size.

6. Conclusion and Discussion

ULIP-2 introduces scalable multimodal 3D pre-training using automatically generated language descriptions and scaled-up representation learning. It improves downstream performance while leaving scene-level understanding and broader labor impacts as important considerations.

  • ULIP-2 uses scalable multimodal triplet creation and large multimodal models to improve multimodal 3D representation learning.
  • ULIP-2’s pre-training primarily targets object-level 3D shape datasets, leaving scene-level 3D understanding for future work.
  • Reducing human annotation labor may affect low-skilled job markets, creating a broader-impact consideration for ULIP-2.

A.1. Ablation on 3D Input

The 3D-input ablation evaluates how color information affects ULIP-2’s Objaverse-LVIS zero-shot classification under OpenShape-compatible preprocessing. The associated setup uses Point-BERT with OpenCLIP ViT-G encoders and joint Objaverse-ShapeNet pre-training.

  • ULIP-2’s color ablation on Objaverse-LVIS uses the same 10k colored-point-cloud preprocessing as OpenShape.The study evaluates how color information influences zero-shot classification.
  • Table 10 evaluates Point-BERT with ULIP-2 after joint Objaverse and ShapeNet pre-training using OpenCLIP ViT-G encoders.

A.2. Different Kinds of 3D Backbones

ULIP-2’s gains extend across different 3D backbones, supporting backbone-agnostic improvements. Point-BERT is used mainly because it is scale-up-friendly and achieves better zero-shot classification results.

  • ULIP-2 significantly improves performance with the intrinsically different PointNeXt backbone.
  • The experiments mainly use Point-BERT because its transformer architecture is scale-up-friendly and delivers better zero-shot classification results.
  • Table 11 reports ModelNet40 zero-shot classification across different 3D backbones with ShapeNet pre-training and SLIP ViT-B encoders.
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