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

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卜聞

,Walker: Self-supervised Multiple Object Tracking by?Walking on?Temporal Appearance Graphs,es of all videos, and instance IDs to associate them through time. To this end, we introduce Walker, the first self-supervised tracker that learns from videos with sparse bounding box annotations, and no tracking labels. First, we design a quasi-dense temporal object appearance graph, and propose a

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CRAMP

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,GPSFormer: A Global Perception and?Local Structure Fitting-Based Transformer for?Point Cloud Underslar point clouds without reliance on external data remains a formidable challenge. To address this problem, we propose ., an innovative .lobal .erception and Local .tructure .itting-based Transf., which learns detailed shape information from point clouds with remarkable precision. The core of GPSFor

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,FSD-BEV: Foreground Self-distillation for?Multi-view 3D Object Detection,friendly perception solution for autonomous driving, there is still a performance gap compared to LiDAR-based methods. In recent years, several cross-modal distillation methods have been proposed to transfer beneficial information from teacher models to student models, with the aim of enhancing perf

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,SceneGraphLoc: Cross-Modal Coarse Visual Localization on?3D Scene Graphs,hs comprise multiple modalities, including object-level point clouds, images, attributes, and relationships between objects, offering a lightweight and efficient alternative to conventional methods that rely on extensive image databases. Given these modalities, the proposed method SceneGraphLoc lear