OSIM通过关注物体层面提升3D场景评估与人类感知的一致性。
Objectness Similarity: Capturing Object-Level Fidelity in 3D Scene Evaluation
- 基于物体检测模型提取特征，量化场景中每个物体的'物体度'
- 用户研究显示其比现有指标更贴近人类视觉感知
- 适合需要精确评估物体质量的3D生成与重建任务
本文提出一种新型3D场景评估指标Objectness SIMilarity（OSIM），聚焦于人类视觉感知的基本单位——物体。现有指标侧重整体图像质量，与人类感知存在偏差。受神经心理学启发，我们假设人类对3D场景的识别本质在于对单个物体的关注。OSIM通过物体检测模型及其特征表示，实现以物体为中心的评估，量化场景中每个物体的'物体度'。用户研究表明，OSIM在感知一致性上优于现有指标。我们还采用多种方法分析了OSIM特性，并在统一实验设置下重新评估了近期3D重建与生成模型，厘清该领域的实际进展。代码已开源：https://github.com/Objectness-Similarity/OSIM。
原文摘要 · Abstract (English)
This paper presents Objectness SIMilarity (OSIM), a novel evaluation metric for 3D scenes that explicitly focuses on "objects," which are fundamental units of human visual perception. Existing metrics assess overall image quality, leading to discrepancies with human perception. Inspired by neuropsychological insights, we hypothesize that human recognition of 3D scenes fundamentally involves attention to individual objects. OSIM enables object-centric evaluations by leveraging an object detection model and its feature representations to quantify the "objectness" of each object in the scene. Our user study demonstrates that OSIM aligns more closely with human perception compared to existing metrics. We also analyze the characteristics of OSIM using various approaches. Moreover, we re-evaluate recent 3D reconstruction and generation models under a standardized experimental setup to clarify advancements in this field. The code is available at https://github.com/Objectness-Similarity/OSIM.
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