ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging Scenes with Subtly Distinguished Objects
This addresses the lack of comprehensive 3D benchmarks for embodied AI, though it is incremental as it builds on existing evaluation needs.
The paper tackles the problem of insufficient evaluation of 3D models in challenging scenes with subtly distinguished objects by proposing ObjVariantEnsemble, a scheme that systematically introduces more scenes with specified object characteristics and uses an LLM-VLM-cooperated annotator to capture key distinctions, resulting in a benchmark that better challenges 3D models and reveals their shortcomings.
3D scene understanding is an important task, and there has been a recent surge of research interest in aligning 3D representations of point clouds with text to empower embodied AI. However, due to the lack of comprehensive 3D benchmarks, the capabilities of 3D models in real-world scenes, particularly those that are challenging with subtly distinguished objects, remain insufficiently investigated. To facilitate a more thorough evaluation of 3D models' capabilities, we propose a scheme, ObjVariantEnsemble, to systematically introduce more scenes with specified object classes, colors, shapes, quantities, and spatial relationships to meet model evaluation needs. More importantly, we intentionally construct scenes with similar objects to a certain degree and design an LLM-VLM-cooperated annotator to capture key distinctions as annotations. The resultant benchmark can better challenge 3D models, reveal their shortcomings in understanding, and potentially aid in the further development of 3D models.