DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis

Advances in radiance fields have enabled photorealistic novelview synthesis. In several domains, large-scale real-world datasets havebeen developed to support comprehensive benchmarking and to facilitateprogress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered imagesper scene remains lacking, limiting the development. To address thisgap, we introduce DF3DV-1K, a large-scale real-world dataset compris-ing 1,048 scenes, each providing clean and cluttered image sets for bench-marking. In total, the dataset contains 89,924 images captured using con-sumer cameras to mimic casual capture, spanning 17 scenario types, 128distractor types, and 161 scene themes across indoor and outdoor envi-ronments. A curated subset of 41 scenes, DF3DV-41, is systematicallydesigned to evaluate the robustness of distractor-free radiance field meth-ods under challenging scenarios. Using DF3DV-1K, we benchmark ninerecent distractor-free radiance field methods and 3D Gaussian Splatting,identifying the most robust methods and the most challenging scenarios.Beyond benchmarking, we demonstrate an application of DF3DV-1K byfine-tuning a diffusion-based 2D enhancer to improve radiance field meth-ods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPSon the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hopeDF3DV-1K facilitates the development of distractor-free vision and pro-motes progress beyond scene-specific approaches. The dataset and leader-board are available at https://johnnylu305.github.io/df3dv1k_web/.