arXiv Machine Learning

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.

By Tayyab Nasir, Daochang Liu, Ajmal Mian

llms rag computer-vision multimodal safety

arXiv Computer Vision

3d ago

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...

By Bingde Liu, Wu Ran, Jinglei Zhang, Huanhuan Yuan, Chao Ma

diffusion computer-vision efficiency benchmarks

arXiv AI

Jul 10

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.

By Zhengqin Li, Cheng Zhang, Jakob Engel, Zhao Dong

llms benchmarks

arXiv Computer Vision

3d ago

DARD: Zero-Shot Degradation-Aware Retinex-Guided Diffusion for Low-Light Image Enhancement

arXiv:2608.29243v1 Announce Type: new Abstract: Existing diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paire...

By Wenjie Cai, Yuezhe Yang, Jianyang Xia, Xingbo Dong, Zhe Jin

diffusion computer-vision benchmarks

arXiv AI

Jul 15

ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning

arXiv:2607. 12433v1 Announce Type: cross Abstract: Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE).

By Zijie Wang, Wei Zhang, Weiming Zhang, Xiao Tan, Weikai Chen, Xiaoxu Li, Guanbin Li

diffusion computer-vision

arXiv AI

Aug 3

WaiT for the Signal: Simple Frequency-Aware Flow-Matching

arXiv:2607. 28760v1 Announce Type: cross Abstract: As image generation models scale to ever higher resolutions, global coherence, local detail, and texture fidelity become critical axes for generation quality.

By Krunoslav Lehman Pavasovic, Th\'eophane Vallaeys, St\'ephane Mallat, Giulio Biroli, Luke Zettlemoyer, Brian Karrer, Jakob Verbeek

llms diffusion benchmarks

arXiv Computer Vision

Aug 24

M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo

arXiv:2608.20788v1 Announce Type: new Abstract: Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or...

By Byeonggwon Lee, Sanggi Lee, Siwoo Lee, Khang Truong Giang, Soohwan Song

benchmarks

arXiv AI

Aug 11

UniSpace: Unified Visual Representation and Scalable Multimodal Modeling

arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.

By Jinbo Yan, Limeng Qiao, Jie Qin, Junyan He, Feize Wu, Guanglu Wan

llms rag diffusion multimodal

arXiv Machine Learning

Aug 21

Decoupling High and Low Frequencies for Faithful Image Generation with Fine Details

arXiv:2509. 05441v4 Announce Type: replace-cross Abstract: Latent generative models compress images into learned embeddings prior to synthesis, and the generation quality critically depends on how faithfully these embeddings preserve visual detail.

By Tejaswini Medi, Hsien-Yi Wang, Arianna Rampini, Margret Keuper

rag diffusion nlp

arXiv AI

Jun 30

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

arXiv:2606. 29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid surfaces.

By Xiaohao Xu, Feng Xue, Xiang Li, Haowei Li, Shusheng Yang, Tianyi Zhang, Matthew Johnson-Roberson, Xiaonan Huang

llms computer-vision benchmarks

arXiv AI

Jun 3

GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance

arXiv:2606. 03119v1 Announce Type: cross Abstract: Guidance methods, such as classifier-free guidance (CFG) and auto-guidance (AG), have advanced noise-to-data generation in diffusion models.

By Zehua Chen, Yucheng Yang, Binjie Yuan, Kaiwen Zheng, Jun S. Liu, Jun Zhu

diffusion

arXiv Machine Learning

Jun 25

Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning

arXiv:2606. 25232v1 Announce Type: new Abstract: Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones.

By Erik Ayari, Manuel Traub, Martin V. Butz

efficiency

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