QuatRoPE: A Scalable Object Relation Encoding Method for 3D Spatial Reasoning in Large Language Models

QuatRoPE is an innovative study accepted by CVPR 2026. It solves the scalability problem in 3D spatial reasoning using Quaternion Rotation Positional Embedding, breaking through the quadratic complexity bottleneck of traditional methods and achieving linear complexity for object relation representation. At the same time, it preserves the original capabilities of large language models via the Isolated Gated RoPE Extension (IGRE) mechanism.

3D spatial reasoning is a core capability of embodied agents, which requires understanding spatial and semantic relationships in scenes to locate targets. However, it faces the bottleneck of scarce 3D scene-language paired data. Therefore, researchers leverage the pre-trained capabilities of large language models to attempt injecting 3D scene representations into the LLM input space.

Current mainstream methods fall into two categories: 1. Encoding absolute position information—premature fusion of object features makes it difficult to extract relative spatial relationships, increasing learning difficulty; 2. Explicitly encoding all spatial relationships—the number of object pairs is N(N-1)/2 (quadratic complexity), leading to inflated input length, high computational cost, and difficulty in handling complex scenes.

Core contributions of QuatRoPE: 1. Linear complexity object relation encoding: Using quaternions to encode 3D coordinates, deferring relation computation to the attention layer for implicit processing—input length is proportional to the number of objects N; 2. Isolated Gated RoPE Extension (IGRE): Restricting QuatRoPE to act on object tokens via a gating mechanism without affecting the original positional encoding of text tokens, enabling plug-and-play extension while preserving the original capabilities of LLMs.

Validated on benchmarks like ScanRefer, Multi3DRefer, and SQA3D: On ScanRefer, Acc@0.5 reaches 52.2% (Chat-Scene) and 52.5% (3DGraphLLM); On Multi3DRefer, F1@0.5 reaches 54.8% and 56.0%; On SQA3D, EM@1 reaches 54.7% and 55.2%, improving object localization and deep scene understanding capabilities.

The research team open-sourced the complete PyTorch implementation on GitHub, including Vicuna-7B-v1.5 pre-training/fine-tuning scripts, support for GT/Mask3D segmentation training workflows, ASR evaluation code, and pre-trained model weights (available on Hugging Face/ModelScope). Environment requirements: Python3.9.17, PyTorch2.7.1; CUDA12.8 acceleration is recommended.

QuatRoPE provides a feasible path for large-scale 3D scene processing, suitable for scenarios like robot navigation and AR/VR; The IGRE mechanism offers a new paradigm for LLM domain adaptation; It highlights the importance of cross-modal research, narrowing the gap between human spatial cognition and machine understanding.

QuatRoPE is a significant advancement in the field of 3D spatial reasoning, balancing scalability and performance, and laying the foundation for next-generation intelligent systems to understand the physical world. It has been accepted by CVPR2026, with code and models open-sourced to provide resources for the community.