ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging Scenes with Subtly Distinguished Objects

TL;DR

This paper introduces ObjVariantEnsemble, a new benchmark for evaluating 3D point cloud models in complex scenes with subtly distinguished objects, aiming to reveal model limitations and guide improvements.

Contribution

The paper presents a systematic scheme to generate challenging 3D scenes with detailed annotations, enhancing the evaluation of 3D models' understanding capabilities in real-world scenarios.

Findings

Benchmark reveals model shortcomings in distinguishing similar objects.

Constructed scenes with varied object attributes increase evaluation difficulty.

Annotations help identify specific areas for model improvement.

Abstract

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.