The paper introduces a new depth image upsampling technique that tackles the low lateral resolution of modern depth cameras by formulating a convex optimization problem with higher‑order regularization. An anisotropic diffusion tensor derived from a high‑resolution intensity image guides the upsampling, and a primal‑dual algorithm is implemented for efficient, multi‑frame‑per‑second performance. Experiments on the Middlebury 2007 datasets demonstrate that this method surpasses state‑of‑the‑art approaches in both speed and accuracy, and the authors also present new datasets with precise ground truth to benchmark depth upsampling using real sensor data.
Dual-pixel (DP) imaging enables metric depth estimation from a single camera using sub-aperture disparity. However, the extremely small effective baseline limits disparity observability, leading to structural degradation and depth failure in textureless, low-contrast, or downsampled regions.
arXiv:2608.28895v1 Announce Type: new Abstract: We introduce ReconSplat, a feed-forward model for 3D scene reconstruction that aims to address the longstanding trade-off between plausible view genera...
The paper introduces AffineGS, a method that improves the geometric accuracy of 3D Gaussian Splatting (3DGS) by applying a spatially varying affine rectification to monocular depth estimates. It identifies that monocular depth networks suffer from region-dependent, piecewise-linear distortions, rendering global alignment ineffective. By employing edge-aware, regionally smooth fields and a normal-aware regularizer, AffineGS aligns disparity with 3DGS depth and normals, achieving strong photometric quality while significantly enhancing geometry across diverse experiments.
arXiv:2609.36168v1 Announce Type: new Abstract: Depth completion aims to recover dense metric depth from sparse sensor measurements, increasingly leveraging visual foundation models as geometric prio...
arXiv:2609.01172v1 Announce Type: new Abstract: Monocular depth estimation has long stood as a fundamental challenge in computer vision, enabling a wide range of applications including 3D reconstruct...
The paper introduces HypoDepth, an event-image monocular depth estimation framework that uses a discrete Depth Hypothesis Volume (DHV) to convert depth regression into a constrained search problem. By building a lightweight 3D cost volume between DHV features and contextual features, the method performs multi-scale correlation search for stable residual optimization, enabling efficient global-to-local refinement across resolutions. Experiments on DSEC and MVSEC show state‑of‑the‑art performance, strong zero‑shot generalization, and real‑time capability on resource‑limited devices.
FlowMoDE introduces a coarse‑to‑fine flow‑matching framework for monocular depth estimation that balances efficiency and detail reconstruction. The method first decodes coarse depth conditioned on diffused features from a pre‑trained semantic encoder, then refines fine structural patterns with expanded decoding layers. Trained only on a synthetic indoor dataset, FlowMoDE achieves a 14.1% lower relative error and a 9.9% lower Chamfer distance compared to state‑of‑the‑art methods across five real‑world indoor and outdoor datasets, and demonstrates strong generalization to realistic robotic scenes.
PXDepth is a monocular depth estimation model that separates global context modeling from pixel-level depth prediction. It uses a large-patch Vision Transformer to capture scene context and a pixel-space predictor with Context‑Modulated Pixel Transformer blocks to preserve high‑resolution spatial details. The approach maintains fine structures and sharp boundaries while achieving competitive global depth accuracy in zero‑shot benchmarks.
arXiv:2610.32800v1 Announce Type: new Abstract: Real-world 3D reconstruction pipelines often rely on multi-view images degraded by lossy compression. While compression artifacts have been extensively...
The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.
Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full $360^{\circ}$ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training.
arXiv:2412.01090v3 Announce Type: replace Abstract: Video depth estimation is essential for applications such as autonomous driving, AR/VR, and robotics. Recent transformer-based single-image monocul...