点击按钮预约直播 深度估计是机器人感知、三维重建、AR/VR 等应用的核心。然而，现有的深度估计方法普遍存在边缘飞点（Flying Pixels）问题，而这会导致机器人执行决策时候，引发错误动作；三维重建时导致物体轮廓鬼影重重等。现有方法经历边 缘飞点主要因为以下原因： 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>直播和内容获取转到 → 自动驾驶之心知识星球 本文提出 Pixel-Perfect Depth (PPD)，一种 直接在像素空间进行扩散生成的单目深度估计模型 ，从根源上避免了因 VAE 压缩导致的伪影问题。然而，高分辨率像素空间的扩散建模极具挑战：模型需兼顾 全局语义的一致性 与 局部细节的精确 性 ，否则极易出现结构失真或深度跳变。为此，本文设计了语义引导的扩散 Transformer（SP-DiT），在扩散过程中引入 来自视觉基础模型的高层语义特征作为提示，有效增强了模型对全局结构的把握与细节恢复能力。同时，本文提出一种 判别式模型 （如 Depth Anything v2, Depth Pro ）由于回归损失的平滑倾向，容易在深度 ...

Research Motivation and Contribution - The core issue in existing depth estimation methods is the "Flying Pixels" problem, which leads to erroneous actions in robotic decision-making and ghosting in 3D reconstruction [2] - The proposed method, Pixel-Perfect Depth (PPD), aims to eliminate artifacts caused by VAE compression by performing diffusion directly in pixel space [6] Innovation and Methodology - PPD introduces a novel diffusion model that operates in pixel space, addressing challenges of maintaining global semantic consistency and local detail accuracy [6][9] - The model incorporates a Semantics-Prompted Diffusion Transformer (SP-DiT) that enhances the modeling capabilities by integrating high-level semantic features during the diffusion process [9][16] Results and Performance - PPD outperforms existing generative depth estimation models across five public benchmarks, showing significant improvements in edge point cloud evaluation and producing depth maps with minimal "Flying Pixels" [14][20] - The model demonstrates exceptional zero-shot generalization capabilities, achieving superior performance without relying on pre-trained image priors [20][22] Experimental Analysis - A comprehensive ablation study indicates that the proposed SP-DiT significantly enhances performance metrics, with an 78% improvement in the AbsRel metric on the NYUv2 dataset compared to baseline models [25][26] - The introduction of a Cascaded DiT design improves computational efficiency by reducing inference time by 30% while maintaining high accuracy [26][27] Edge Point Cloud Evaluation - The model aims to generate pixel-perfect depth maps, addressing the challenge of evaluating edge accuracy through a newly proposed Edge-Aware Point Cloud Metric [28][30] - Experimental results confirm that PPD effectively avoids the "Flying Pixels" issue, demonstrating superior performance in edge accuracy compared to existing methods [28][34] Conclusion - PPD represents a significant advancement in depth estimation, providing high-quality outputs with sharp structures and clear edges, while minimizing artifacts [34][35] - The research opens new avenues for high-fidelity depth estimation based on diffusion models, emphasizing the importance of maintaining both global semantics and local geometric consistency [35]