$D^3$-RSMDE: 40$\times$ Faster and High-Fidelity Remote Sensing Monocular Depth Estimation

• URL: http://arxiv.org/abs/2603.16362v1
• Date: Tue, 17 Mar 2026 10:50:36 GMT
• Title: $D^3$-RSMDE: 40$\times$ Faster and High-Fidelity Remote Sensing Monocular Depth Estimation
• Abstract summary: Real-time, high-fidelity monocular depth estimation from remote sensing imagery is crucial for numerous applications.<n>Although using Vision Transformer (ViT) backbones for dense prediction is fast, they often exhibit poor perceptual quality.<n>We propose Depth Detail Diffusion for Remote Sensing Monocular Depth Estimation ($D3$-RSMDE) to overcome these limitations.<n>$D3$-RSMDE achieves a notable 11.85% reduction in the Learned Perceptual Image Patch Similarity (LPIPS) perceptual metric.
• Score: 72.9912717963138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time, high-fidelity monocular depth estimation from remote sensing imagery is crucial for numerous applications, yet existing methods face a stark trade-off between accuracy and efficiency. Although using Vision Transformer (ViT) backbones for dense prediction is fast, they often exhibit poor perceptual quality. Conversely, diffusion models offer high fidelity but at a prohibitive computational cost. To overcome these limitations, we propose Depth Detail Diffusion for Remote Sensing Monocular Depth Estimation ($D^3$-RSMDE), an efficient framework designed to achieve an optimal balance between speed and quality. Our framework first leverages a ViT-based module to rapidly generate a high-quality preliminary depth map construction, which serves as a structural prior, effectively replacing the time-consuming initial structure generation stage of diffusion models. Based on this prior, we propose a Progressive Linear Blending Refinement (PLBR) strategy, which uses a lightweight U-Net to refine the details in only a few iterations. The entire refinement step operates efficiently in a compact latent space supported by a Variational Autoencoder (VAE). Extensive experiments demonstrate that $D^3$-RSMDE achieves a notable 11.85% reduction in the Learned Perceptual Image Patch Similarity (LPIPS) perceptual metric over leading models like Marigold, while also achieving over a 40x speedup in inference and maintaining VRAM usage comparable to lightweight ViT models.

Related papers

• SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation [10.518150741228862]
  SlimDiffSR is a lightweight and efficient diffusion-based framework for real-world remote sensing image super-resolution.<n>It attains up to $200times$ inference acceleration and a $20times$ reduction in model parameters compared with multi-step diffusion models.
  arXiv  Detail & Related papers  (2026-05-04T03:54:54Z)
• NanoSD: Edge Efficient Foundation Model for Real Time Image Restoration [5.158202521463481]
  NanoSD is a general-purpose diffusion foundation model family suitable for real-time visual generation and restoration on edge devices.<n>We show how architectural balance, feature routing, and latent-space preservation jointly shape true on-device latency.<n>When used as a drop-in backbone, NanoSD enables state-of-the-art performance across image super-resolution, image deblurring, face restoration, and monocular depth estimation.
  arXiv  Detail & Related papers  (2026-01-14T19:30:53Z)
• Bridging Robustness and Efficiency: Real-Time Low-Light Enhancement via Attention U-Net GAN [0.0]
  We propose a hybrid Attention U-Net GAN that provides generative-level texture recovery at edge-deployable speeds.<n>Our method achieves a best-in-class LPIPS score of 0.112 among efficient models.<n>This represents a 40x speedup over latent diffusion models, making our approach suitable for near real-time applications.
  arXiv  Detail & Related papers  (2026-01-10T10:39:22Z)
• DiP: Taming Diffusion Models in Pixel Space [91.51011771517683]
  Diffusion Transformer (DiT) backbone operates on large patches for efficient global structure construction.<n>Co-trained lightweight Patch Detailer Head leverages contextual features to restore fine-grained local details.
  arXiv  Detail & Related papers  (2025-11-24T06:55:49Z)
• Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency [60.74505433956616]
  continuous-time consistency model (sCM) is theoretically principled and empirically powerful for accelerating academic-scale diffusion.<n>We first develop a parallelism-compatible FlashAttention-2 JVP kernel, enabling sCM training on models with over 10 billion parameters and high-dimensional video tasks.<n>We propose the score-regularized continuous-time consistency model (rCM), which incorporates score distillation as a long-skip regularizer.
  arXiv  Detail & Related papers  (2025-10-09T16:45:30Z)
• PocketSR: The Super-Resolution Expert in Your Pocket Mobiles [69.26751136689533]
  Real-world image super-resolution (RealSR) aims to enhance the visual quality of in-the-wild images, such as those captured by mobile phones.<n>Existing methods leveraging large generative models demonstrate impressive results, but the high computational cost and latency make them impractical for edge deployment.<n>We introduce PocketSR, an ultra-lightweight, single-step model that brings generative modeling capabilities to RealSR while maintaining high fidelity.
  arXiv  Detail & Related papers  (2025-10-03T13:56:18Z)
• LiftVSR: Lifting Image Diffusion to Video Super-Resolution via Hybrid Temporal Modeling with Only 4$\times$RTX 4090s [16.456543112614586]
  Diffusion models have advanced video super-resolution (VSR) by enhancing perceptual quality.<n>We propose LiftVSR, an efficient VSR framework that leverages and mitigates the image-wise diffusion prior to PixArt-$alpha$, achieving state-of-the-art results.<n>Experiments on several typical VSR benchmarks have demonstrated that LiftVSR achieves impressive performance with significantly lower computational costs.
  arXiv  Detail & Related papers  (2025-06-10T07:49:33Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses [1.086544864007391]
  We develop approaches to the rectification and cost volume that consider ML acceleration (GPU and NPUs) in recent hardware.<n>Based on our approaches, we develop MultiHeadDepth and HomoDepth.<n>HomoDepth, which can directly process unrectified images, reduces the end-to-end latency by 44.5%.
  arXiv  Detail & Related papers  (2024-11-15T07:43:45Z)
• Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff [26.566339984225756]
  Existing salient object detection methods often adopt deeper and wider networks for better performance. We propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches. We show that our method achieves better efficiency-accuracy balance across five benchmarks.
  arXiv  Detail & Related papers  (2023-01-17T03:43:25Z)
• A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection [89.88222217065858]
  We design a single stream network to use the depth map to guide early fusion and middle fusion between RGB and depth. This model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $384 times 384$ image.
  arXiv  Detail & Related papers  (2020-07-14T04:40:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.