arXiv Computer Vision
WAVE: Wavelet-Controlled Coarse-to-Fine Guidance for Depth Super-Resolution
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Machine Learning
Aug 27
WAVE: Reversing the Guidance Hierarchy for Coarse-to-Fine Guided Depth Super-Resolution
WAVE introduces a multi-level discrete wavelet transform (ML‑DWT) to reverse the typical fine‑to‑coarse bias in guided depth super‑resolution. By consuming wavelet sub‑bands and semantic tokens in reverse order, it separates structure and detail reconstruction, applies semantic gating to high‑frequency bands, and fuses modalities via an invertible coupling mechanism. Experiments on multiple benchmarks show that WAVE matches or outperforms existing methods, especially at high upsampling factors where low‑resolution depth has minimal structure.
arXiv Machine Learning
Aug 26
NAIMA: Semantics Aware RGB Guided Depth Super-Resolution
The paper introduces NAIMA, a guided depth super‑resolution framework that leverages global contextual semantic priors from pretrained vision transformer token embeddings. Its Guided Token Attention (GTA) module uses depth encodings as queries to attend over semantic tokens, with a zero‑initialized gate controlling the influence of semantic evidence. NAIMA achieves competitive in‑distribution performance while delivering superior cross‑dataset generalization without relying on decoded priors or auxiliary objectives.
arXiv Computer Vision
Sep 17
GeoCueFormer: Geometry-Guided Wavelet Representation and Prediction-Cued Dual-Stage Decoder for Underwater Semantic Segmentation
arXiv:2609.18069v1 Announce Type: new Abstract: Underwater semantic segmentation is essential for marine ecosystem monitoring, yet remains challenging due to severe visual degradation. Light absorpti...
arXiv AI
6d ago
Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation
Wavelet Phase Diffusion (ψ‑PD) is a new simulation‑to‑real translation method that operates in the Dual‑Tree Complex Wavelet Packet Transform domain, allowing spatially adaptive phase injection without global spectral interference. It also introduces Low‑Frequency Randomization to decouple the model from synthetic illumination priors, enabling realistic, in‑distribution real‑world appearance while preserving structural and semantic consistency. Trained on unpaired open‑domain data, ψ‑PD achieves superior realism and semantic consistency on vKITTI→KITTI image translation and improves CARLA video translation performance, reducing planner errors by over 5%.
arXiv Computer Vision
Sep 1
Efficient and High-Quality Depth Estimation via Pixel-Space Diffusion with Linear Attention
arXiv:2608.30129v1 Announce Type: new Abstract: This work presents $\textbf{Lapis}$, a $\textbf{l}$inear-$\textbf{a}$ttention-based $\textbf{pi}$xel-$\textbf{s}$pace generative framework that achieve...
arXiv Computer Vision
Oct 27
FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation
arXiv:2509.01107v2 Announce Type: replace Abstract: Layout-to-image (L2I) generation has exhibited promising results in natural domains, but suffers from limited generative fidelity and weak alignmen...