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

Qihang Cao, Huangxun Chen

TL;DR

This work addresses the lack of comprehensive 3D benchmarks for scene-level point clouds by introducing ObjVariantEnsemble (OVE), a data-generation framework that constructs challenging scenes with subtly distinguished objects and a cooperative LLM-VLM annotator to produce fine-grained distinctions. OVE combines object-retrieval, scene integration, and distinction annotation to create 75k new scenes and rich annotations, enabling rigorous evaluation of 3D grounding and spatial reasoning. The authors show that current 3D understanding models struggle with pure spatial reasoning, particularly when relying on location cues alone, and provide insights that shape future improvements in 3D encoders and position representations. The benchmark has practical implications for advancing embodied AI and robotics by offering a scalable, customizable, and richly annotated platform for evaluating and improving 3D perception in complex scenes.

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.

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

TL;DR

This work addresses the lack of comprehensive 3D benchmarks for scene-level point clouds by introducing ObjVariantEnsemble (OVE), a data-generation framework that constructs challenging scenes with subtly distinguished objects and a cooperative LLM-VLM annotator to produce fine-grained distinctions. OVE combines object-retrieval, scene integration, and distinction annotation to create 75k new scenes and rich annotations, enabling rigorous evaluation of 3D grounding and spatial reasoning. The authors show that current 3D understanding models struggle with pure spatial reasoning, particularly when relying on location cues alone, and provide insights that shape future improvements in 3D encoders and position representations. The benchmark has practical implications for advancing embodied AI and robotics by offering a scalable, customizable, and richly annotated platform for evaluating and improving 3D perception in complex scenes.

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.

Paper Structure

This paper contains 22 sections, 1 equation, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. 3D Point Cloud Benchmarks
  4. Point Cloud LLM
  5. Benchmark Data Construction
  6. OVE Overview
  7. Object Retriever & Scene Integrator
  8. Object Collator
  9. Object Retriever
  10. Scene Integrator
  11. Distinction Recognizer & Annotator
  12. Distinction Recognizer
  13. Distinction Annotator
  14. OVE Benchmark Summary
  15. Benchmark on ObjVariantEnsemble
  16. ...and 7 more sections

Figures (8)

  • Figure 1: Comparison of 3D grounding benchmarks in challenging scenes: (a) one scene in ScanNet/ScanRef where the text is insufficient to accurately locate a chair. (b) one scene in ObjVariantEnsemble where one model accurately identifies targets with sufficient descriptions.
  • Figure 2: OVE Benchmark Construction Overview.
  • Figure 3: ObjVariantEnsemble Scene Data Generation Framework. (For clearer illustration here, we only plot the target and distractors in the scene, without other background objects.)
  • Figure 4: Process for Capturing Annotations with Key Distinguishing Information. We render multi-view images and use LLAVAliu2024visual to extract differences from various perspectives. A LLM is then employed to generate questions based on previous Q&A interactions. Finally, we use LLM to summarize the key differences from all descriptions.
  • Figure 5: OVE Benchmark Summary
  • ...and 3 more figures