PiSA: A Self-Augmented Data Engine and Training Strategy for 3D Understanding with Large Models
This work addresses the data scarcity and evaluation issues in 3D MLLMs for 3D understanding, which is incremental as it builds on existing methods like PointLLM.
The paper tackles the problem of limited and low-quality 3D datasets hindering 3D Multimodal Large Language Models (MLLMs) by introducing PiSA-Engine, a framework for generating enriched 3D instruction data, and PiSA-Bench, a comprehensive benchmark, resulting in state-of-the-art performance with improvements of 46.45% (+8.33%) in zero-shot 3D object captioning and 63.75% (+16.25%) in generative classification.
3D Multimodal Large Language Models (MLLMs) have recently made substantial advancements. However, their potential remains untapped, primarily due to the limited quantity and suboptimal quality of 3D datasets. Current approaches attempt to transfer knowledge from 2D MLLMs to expand 3D instruction data, but still face modality and domain gaps. To this end, we introduce PiSA-Engine (Point-Self-Augmented-Engine), a new framework for generating instruction point-language datasets enriched with 3D spatial semantics. We observe that existing 3D MLLMs offer a comprehensive understanding of point clouds for annotation, while 2D MLLMs excel at cross-validation by providing complementary information. By integrating holistic 2D and 3D insights from off-the-shelf MLLMs, PiSA-Engine enables a continuous cycle of high-quality data generation. We select PointLLM as the baseline and adopt this co-evolution training framework to develop an enhanced 3D MLLM, termed PointLLM-PiSA. Additionally, we identify limitations in previous 3D benchmarks, which often feature coarse language captions and insufficient category diversity, resulting in inaccurate evaluations. To address this gap, we further introduce PiSA-Bench, a comprehensive 3D benchmark covering six key aspects with detailed and diverse labels. Experimental results demonstrate PointLLM-PiSA's state-of-the-art performance in zero-shot 3D object captioning and generative classification on our PiSA-Bench, achieving significant improvements of 46.45% (+8.33%) and 63.75% (+16.25%), respectively. We will release the code, datasets, and benchmark.