PointLLM

Why we included this project

PointLLM is worth a close look if you want to see how a large language model can work directly on raw 3D point clouds instead of images. It feeds colored object scans into an LLM backbone, so the model can name object types, describe geometric structure and appearance, and sidestep the occlusion and viewpoint problems that trip up camera-based recognition. The repo includes the full training pipeline, 660K simple and 70K complex instruction pairs, inference code with checkpoints, and evaluation scripts for the 3D object classification and captioning benchmarks, plus a Gradio demo, which makes it a practical reference for reproducing the ECCV 2024 results. Researchers and teams working on embodied AI, robotics, or spatial understanding can adapt its two-stage training strategy to their own point-cloud data, though expect a heavy training run and a pinned PyTorch and Transformers environment.