Cheng-You Lu, Yi-Shan Hung, Wei-Ling Chi, Hao-Ping Wang, Charlie Li-Ting Tsai, Yu-Cheng Chang, Yu-Lun Liu, Thomas Do, Chin-Teng Lin
4 min
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 image sets for benchmarking. In total, the dataset contains 89,924 images captured using consumer cameras to mimic casual capture, spanning 128 distractor types and 161 scene themes across indoor and outdoor environments. A curated subset of 41 scenes, DF3DV-41, is systematically designed to evaluate the robustness of distractor-free radiance field methods under challenging scenarios. Using DF3DV-1K, we benchmark nine recent 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 by fine-tuning a diffusion-based 2D enhancer to improve radiance field methods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPS on the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hope DF3DV-1K facilitates the development of distractor-free vision and promotes progress beyond scene-specific approaches. The dataset and leaderboard are available at https://johnnylu305.github.io/df3dv1k_web/.
Alex: So it's quality control in the training process itself.
Sam: Exactly. And that's what makes the benchmark meaningful. When the authors tested nine different existing methods against this dataset, they found that even the best systems still struggled with certain scenarios — nighttime scenes and fluid or unpredictable motion were particular weak points. That's a useful finding. It tells the field precisely where the gaps are, rather than just confirming that progress has been made.
Alex: So the paper is as much about mapping the problem clearly as it is about solving it.
Sam: That's a fair reading. By establishing this benchmark, the authors give future researchers a shared measuring stick — a way to compare approaches honestly and identify where the next improvements need to come from. It's a meaningful contribution, not because it closes the problem, but because it defines it more precisely than before.
Alex: Thanks for listening to ResearchPod.