Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

Eructation

,An Optimization Framework to Enforce Multi-view Consistency for Texturing 3D Meshes,aches typically use diffusion models to aggregate multi-view inputs, where common issues are the blurriness caused by the averaging operation in the aggregation step or inconsistencies in local features. This paper introduces an optimization framework that proceeds in four stages to achieve multi-vi

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STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians,neration with spatial-temporal consistency remains a challenge. In this work, we propose STAG4D, a novel framework that combines pre-trained diffusion models with dynamic 3D Gaussian splatting for high-fidelity 4D generation. Drawing inspiration from 3D generation techniques, we utilize a multi-view

Osmosis

ADORE

Efficient NeRF Optimization - Not All Samples Remain Equally Hard,uality for many 3D reconstruction and rendering tasks but require substantial computational resources. The encoding of the scene information within the NeRF network parameters necessitates stochastic sampling. We observe that during the training, a major part of the compute time and memory usage is

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,Revisiting Calibration of Wide-Angle Radially Symmetric Cameras,se end-to-end approaches are typically tethered to one fixed camera model, leading to issues: . lack of flexibility, necessitating network architectural changes and retraining when changing camera models; . reduced accuracy, as a single model limits the diversity of cameras represented in the traini

Carcinoma

,Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs,es to produce final output images encoded in a standard color space (e.g., sRGB). Neural-based end-to-end learnable ISPs offer promising advancements, potentially replacing traditional ISPs with their ability to adapt without requiring extensive tuning for each new camera model, as is often the case

农学

Memorial

,Revisiting Domain-Adaptive Object Detection in Adverse Weather by the Generation and Composition of in adverse weather conditions. Despite significant progress, existing methods are still plagued by low-quality pseudo-labels in degraded images. This paper proposes a generation-composition paradigm training framework that includes the tiny-object-friendly loss, i.e., IAoU loss with a joint-filteri

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Noise Calibration: Plug-and-Play Content-Preserving Video Enhancement Using Pre-trained Video Diffuenting a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and consistenc