PULLM: A Multimodal Framework for Enhanced 3D Point Cloud Upsampling Using Large Language Models

Point cloud upsampling is a critical task in 3D computer vision, aiming to generate dense and uniformly distributed point sets from sparse inputs. While current self-supervised methods show promise, they often struggle with preserving fine-grained geometric details, especially for highly sparse point clouds. To address these limitations, we propose PointUpsampleLLM (PULLM), a novel multimodal framework that leverages the power of large language models (LLMs) to enhance 3D point cloud upsampling. PULLM integrates a pretrained Point Cloud LLM (PointLLM) with visual features extracted from point clouds, learning a unified representation that captures both geometric and semantic information. At the core of our approach is the Feature Aware Translator (FAT) module, which effectively bridges the modality gap between visual and textual features, enhancing the spatial understanding of the LLM. PULLM generates textual descriptions of point clouds on-the-fly, eliminating the need for large paired datasets. Extensive experiments on the PU1K and PUGAN benchmarks demonstrate that PULLM consistently outperforms state-of-the-art methods, achieving significant improvements in Chamfer Distance, Hausdorff Distance, and Point-to-Plane distance metrics. For instance, on the PUGAN dataset with sparse inputs, PULLM achieves a 56.15% improvement in Chamfer Distance over the best baseline. Our qualitative results further illustrate PULLM's superior ability to preserve fine details and generate high-quality upsampled point clouds across various object types and geometries.