arXiv:2604.13416v2 Announce Type: replace-cross Abstract: Advances in radiance fields have enabled photorealistic novel view synthesis. In several domains, large-scale real-world datasets have been developed to support comprehensive benchmarking and to facilitate progress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered images per scene remains lacking, limiting the development. To address this gap, we introduce DF3DV-1K, a large-scale real-world dataset comprising 1,048 scenes, each providing clean and cluttered
Advances in radiance fields have reached a point where high-quality, large-scale datasets are becoming the next critical bottleneck for further development and comprehensive benchmarking in novel view synthesis.
Large-scale, high-quality datasets like DF3DV-1K accelerate the development of more robust and photorealistic novel view synthesis, which is crucial for applications across AR/VR, simulation, and digital twins.
The availability of a substantial, real-world dataset directly addresses a limitation in developing distractor-free radiance fields, enabling more sophisticated and less scene-specific NVS models.
- · AI researchers (CV, NVS)
- · Metaverse platforms
- · AR/VR hardware manufacturers
- · 3D content creators
- · Developers reliant on scene-specific reconstruction methods
- · Companies with proprietary but small datasets
Improved performance and generalization of novel view synthesis models due to better training data.
Faster adoption and broader application of photorealistic virtual environments and digital twins.
Enhanced AI agents leveraging advanced 3D perception could operate more effectively in complex simulated or augmented realities.
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Read at arXiv cs.AI