Objectness Similarity: Capturing Object-Level Fidelity in 3D Scene Evaluation

2025-09-11

AI Summary

Existing 3D scene evaluation metrics predominantly rely on holistic image-quality measures, exhibiting substantial misalignment with human visual perception. To address this, we propose Objectness SIMilarity (OSIM), the first object-level assessment metric grounded in *objectness*—a cognitive principle reflecting human perceptual prioritization of coherent, semantically meaningful entities. OSIM leverages pre-trained object detectors to extract instance-aware features, then quantifies fidelity via neurocognitively inspired objectness modeling and cross-scene feature similarity computation, enabling end-to-end object-level evaluation. Its core innovations are: (i) shifting evaluation granularity from pixel- or image-level to object-level, and (ii) introducing cognition-driven objectness as a foundational concept for perceptual assessment. Extensive experiments demonstrate that OSIM achieves significantly higher correlation with human subjective ratings than PSNR, LPIPS, and DINO. When re-evaluating twelve state-of-the-art 3D reconstruction and generation models under a unified benchmark, OSIM rankings better reflect perceptual realism.

Technology Category

• Computer Vision: 3D Computer Vision
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Evaluating 3D scenes with object-level fidelity metrics
• Aligning 3D evaluation metrics with human visual perception
• Quantifying objectness in 3D reconstruction and generation models

Innovation

• Object-centric evaluation metric OSIM
• Leverages object detection model features
• Quantifies objectness for human perception alignment