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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Die Mathematik der Compact Discartments. We validate that existing approaches for floor plan generation, while effective in simpler scenarios, cannot yet seamlessly address the challenges posed by MSD. Our benchmark calls for new research in floor plan machine understanding. Code and data are open.

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AWOL: Analysis WithOut Synthesis Using Language,, imagine creating a specific type of tree using procedural graphics or a new kind of animal from a statistical shape model. Our key idea is to leverage language to control such existing models to produce novel shapes. This involves learning a mapping between the latent space of a vision-language mo

inspiration

,OneVOS: Unifying Video Object Segmentation with?All-in-One Transformer Framework,cts aggregation. Recent advanced models either employ a discrete modeling for these components in a sequential manner, or optimize a combined pipeline through substructure aggregation. However, these existing explicit staged approaches prevent the VOS framework from being optimized as a unified whol

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,M3DBench: Towards Omni 3D Assistant with?Interleaved Multi-modal Instructions,er, the majority of existing 3D vision-language datasets and methods are often limited to specific tasks, limiting their applicability in diverse scenarios. The recent advance of .arge .anguage .odels (LLMs) and .ulti-modal .anguage .odels (MLMs) has shown mighty capability in solving various langua

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,MSD: A Benchmark Dataset for?Floor Plan Generation of?Building Complexes,floor plan datasets predominantly feature simple floor plan layouts, typically representing single-apartment dwellings only. To compensate for the mismatch between current datasets and the real world, we develop . (MSD) – the first large-scale floor plan dataset that contains a significant share of

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,LetsMap: Unsupervised Representation Learning for?Label-Efficient Semantic BEV Mapping,g. However, most BEV mapping approaches employ a fully supervised learning paradigm that relies on large amounts of human-annotated BEV ground truth data. In this work, we address this limitation by proposing the first unsupervised representation learning approach to generate semantic BEV maps from

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