Source-linked AI summary
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
Ziyu Guo, Renrui Zhang, Xiangyang Zhu, Yiwen Tang, Xianzheng Ma, Jiaming Han, Kexin Chen, Peng Gao, Xianzhi Li, Hongsheng Li, Pheng-Ann Heng
TL;DR
Existing 3D methods lack a unified framework spanning multiple modalities for understanding, generation, and instruction following. The paper introduces Point-Bind to align point clouds with multi-modal embeddings and Point-LLM to extend this alignment to bilingual 3D instruction following; the systems support broad 3D multi-modal applications and question answering without 3D instruction data.
Problem
A unified 3D framework aligning point clouds with multiple modalities for general understanding, generation, and instruction following remains an open question.
Method
Point-Bind learns a joint embedding space by aligning 3D point clouds with ImageBind modalities, while Point-LLM injects these semantics into LLaMA through parameter-efficient fine-tuning.
Results
Point-Bind supports any-to-3D generation, embedding-space arithmetic, and state-of-the-art 3D zero-shot understanding; Point-LLM provides bilingual 3D instruction following and cross-modal reasoning without 3D instruction data.
Takeaways & Limitations
The framework extends point-cloud learning to multi-modal 3D applications, including generation, open-world understanding, and instruction following.
Takeaways & Limitations
The training data excludes audio for categories that inherently cannot make sound, such as bottles, planters, and couches.
Abstract
from arXiv · showhide
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space between 3D and multi-modalities, enabling many promising applications, e.g., any-to-3D generation, 3D embedding arithmetic, and 3D open-world understanding. On top of this, we further present Point-LLM, the first 3D large language model (LLM) following 3D multi-modal instructions. By parameter-efficient fine-tuning techniques, Point-LLM injects the semantics of Point-Bind into pre-trained LLMs, e.g., LLaMA, which requires no 3D instruction data, but exhibits superior 3D and multi-modal question-answering capacity. We hope our work may cast a light on the community for extending 3D point clouds to multi-modality applications. Code is available at https://github.com/ZiyuGuo99/Point-Bind_Point-LLM.
1. Introduction
The paper addresses the open question of building a unified 3D framework aligned with multiple modalities. It introduces Point-Bind for shared 3D-multimodal representations and Point-LLM for bilingual 3D instruction following.
- A unified 3D framework remains needed because prior approaches are limited by available training modalities and cannot support general multi-modal learning.
- Point-Bind aligns point clouds with images, video, language, and audio in a joint embedding space guided by ImageBind.It trains a 3D encoder with contrastive learning against frozen ImageBind modality encoders using 3D-image-text-audio pairs.
- Point-Bind supports any-to-3D generation, including text/image/audio/point-to-mesh synthesis, beyond existing text-to-3D methods.
- Point-Bind features 3D embedding-space arithmetic, where combining 3D and other-modal features enables composed cross-modal retrieval.
- Point-Bind attains state-of-the-art 3D zero-shot classification and supports audio-referred 3D open-world understanding alongside text reference.
- Point-LLM is a 3D LLM that follows English and Chinese instructions with point-cloud conditions and performs cross-modal reasoning without 3D instruction data.It bridges Point-Bind and LLaMA using a bind network, visual cache, and parameter-efficient fine-tuning, while using public vision-language data.
2. Related Work
Related work has expanded 3D learning through multi-modal representations, large pre-trained models, and self-supervised pre-training. Existing approaches include projecting point clouds into 2D, transferring vision-language embeddings, guiding generation, and reconstructing point clouds.
- Multi-modality Learning: Multi-modal learning jointly models images, videos, text, and audio to improve representation learning and cross-modal downstream performance.
- Large Models in 3D: 3D large-model methods use depth-map projections, 2D-to-3D initialization, or contrastive pre-training guided by vision-language embeddings.
- Large Models in 3D: CLIP-guided methods support text-conditioned generation of 3D objects and stylized meshes, while language models assist 3D spatial-geometry understanding.
- Pre-training in 3D: Self-supervised 3D pre-training addresses limited out-of-domain generalization through encoder-decoder models that reconstruct point clouds.
3. Point-Bind
Point-Bind aligns 3D point clouds with image, text, and audio representations in ImageBind’s joint embedding space through paired-data contrastive learning. This shared space supports cross-modal retrieval, any-to-3D generation, embedding arithmetic, and zero-shot 3D understanding.
- 3.3. Aligning 3D with Multi-modality: Point-Bind uses ImageBind’s pretrained joint space to align point clouds with image, text, and audio modalities through contrastive learning.A trainable 3D encoder is projected into ImageBind’s embedding space, while the corresponding multimodal encoders remain frozen.
- 3.2. Training Data: The training data combines 3D-image-text triplets from ShapeNet with 3D-audio pairs from ShapeNet and ESC-50.The resulting unified dataset contains paired 3D, image, audio, and text inputs for simultaneous contrastive learning.
- 3.4. Multi-modal Applications: Point-Bind enables any-to-3D generation by connecting its multimodal encoders to pretrained CLIP-based text-to-3D decoders without further training.The paper demonstrates synthesizing a 3D car mesh from an input car horn.
- 3.4. Multi-modal Applications: Adding 3D and non-3D embeddings supports composed cross-modal retrieval, such as retrieving beach-car scenes from car and sea-wave embeddings.A laptop embedding combined with keyboard-typing audio similarly retrieves an image of someone working with a laptop.
- 3.4. Multi-modal Applications: Point-Bind attains state-of-the-art 3D zero-shot classification and supports audio-referred recognition of novel 3D categories.The audio-referred setting extends open-world understanding beyond text-based references.
4. Point-LLM
Point-LLM extends Point-Bind into a 3D instruction-following LLM by connecting it to LLaMA through parameter-efficient vision-language tuning. It uses no 3D instruction dataset and supports 3D question answering and cross-modal reasoning.
- 4.1. 3D Instruction-following Capacity: Point-LLM requires no 3D instruction-following dataset because Point-Bind’s shared embedding space transfers alignment from vision-language tuning to 3D.Training uses public vision-language data to teach LLaMA 3D-conditioned response capacity.
- 4.1. 3D Instruction-following Capacity: Parameter-efficient tuning bridges ImageBind and LLaMA with a bind network, zero-initialized gating, and partial parameter updates.Most LLaMA parameters remain frozen while gating factors and bias-norm weights are learned.
- 4.1. 3D Instruction-following Capacity: During inference, a visual cache retrieves similar ImageBind image features and adds their aggregated values to the encoded 3D feature through a residual connection.The cache is intended to reduce the discrepancy between image-encoder training and 3D-encoder inference.
- 4.1. 3D Instruction-following Capacity: Point-LLM can combine Point-Bind 3D features with ImageBind image or audio features to generate responses using information from all input modalities.This enables cross-modal reasoning for language responses.
5. Experiments
Experiments evaluate Point-Bind and Point-LLM across multimodal instruction following, retrieval, embedding arithmetic, any-to-3D generation, zero-shot classification, and design ablations. Results show strong multimodal reasoning, cross-modal retrieval, semantic composition, generation, and open-world recognition, while parameter- and data-efficient tuning supports Point-LLM.
- Implementation and Ablation: Point-Bind uses a two-linear-layer projection network and frozen ImageBind encoders, while Point-LLM uses parameter- and data-efficient tuning without 3D instruction data.Point-LLM tuning includes zero-initialized gating, LoRA, and bias-norm tuning with vision-language data.
- Point-LLM for 3D Q&A: Point-LLM follows English and Chinese instructions while incorporating global and local point-cloud geometry into detailed responses.It also reasons jointly over point clouds with images or audio.
- 3D Cross-modal Retrieval: Point-Bind achieves state-of-the-art performance across 3D-to-3D, 2D-to-3D, 3D-to-2D, and text-to-3D retrieval benchmarks.For 2D-to-3D and text-to-3D retrieval, it improves over ULIP by +14.29% and +13.99%, respectively.
- Embedding-space Arithmetic with 3D: Point-Bind combines 3D and audio embeddings to retrieve images whose semantics reflect both inputs, such as dogs by the sea and bears near water.The results support direct cross-modal embedding composition.
- Any-to-3D Generation: Point-Bind generates satisfactory 3D meshes from text, audio, and point-cloud prompts using its aligned multimodal embedding space.The generation pipeline replaces CLIP’s text encoder with Point-Bind and ImageBind multimodal encoders without further training.
- 3D Zero-shot Understanding: Point-Bind surpasses existing methods on ModelNet40 3D zero-shot classification, indicating strong emergent open-world recognition.The classification head compares point-cloud embeddings with textual class embeddings.
6. Conclusion
The paper introduces Point-Bind to align point clouds with multiple modalities and derives Point-LLM for bilingual 3D instruction following. The framework supports multimodal 3D applications, while future work targets more diverse scene data.
- 6. Conclusion: Point-Bind aligns 3D point clouds with multimodal data in a joint embedding space guided by ImageBind.The resulting framework supports any-to-3D generation, embedding arithmetic, and open-world understanding.
- 6. Conclusion: Point-LLM extends the aligned representation to 3D instruction following in both English and Chinese.It is presented as the first 3D LLM with this instruction-following capability.
- 6. Conclusion: Future work will align multimodality with more diverse 3D data, including indoor and outdoor scenes.The stated goal is to support wider application scenarios.