We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input. Unlike existing indoor 3D detection methods that struggle to model scene geometry, our method makes novel use of NeRF in an end-to-end manner to explicitly estimate 3D geometry, thereby improving 3D detection performance. Specifically, to avoid the significant extra latency associated with per-scene optimization of NeRF, we introduce sufficient geometry priors to enhance the generalizability of NeRF-MLP. Furthermore, we subtly connect the detection and NeRF branches through a shared MLP, enabling an efficient adaptation of NeRF to detection and yielding geometry-aware volumetric representations for 3D detection. Our method outperforms state-of-the-arts by 3.9 mAP and 3.1 mAP on the ScanNet and ARKITScenes benchmarks, respectively. We provide extensive analysis to shed light on how NeRF-Det works. As a result of our joint-training design, NeRF-Det is able to generalize well to unseen scenes for object detection, view synthesis, and depth estimation tasks without requiring per-scene optimization. Code is available at \url{https://github.com/facebookresearch/NeRF-Det}.

翻译：我们提出了NeRF-Det，一种以位姿RGB图像为输入的室内三维检测新方法。与现有难以建模场景几何的室内三维检测方法不同，我们的方法以端到端的方式创新性地利用NeRF显式估计三维几何，从而提升三维检测性能。具体而言，为避免NeRF逐场景优化带来的显著额外延迟，我们引入充分的几何先验增强NeRF-MLP的泛化能力。此外，我们通过共享MLP巧妙连接检测分支与NeRF分支，实现NeRF对检测任务的高效适配，并生成用于三维检测的几何感知体素表示。我们的方法在ScanNet和ARKITScenes基准上分别以3.9 mAP和3.1 mAP的绝对优势超越现有最佳方法。我们通过大量分析揭示NeRF-Det的工作原理。得益于联合训练设计，NeRF-Det能够很好地泛化到未见场景，完成物体检测、视图合成和深度估计任务，且无需逐场景优化。代码已开源至 \url{https://github.com/facebookresearch/NeRF-Det}。