论文标题
朝着高保真的单视图整体重建室内场景
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes
论文作者
论文摘要
我们提出了一个新的框架，以重建整体3D室内场景，包括单视图像的房间背景和室内对象。由于室内场景的严重阻塞，现有方法只能产生具有有限几何质量的室内物体的3D形状。为了解决这个问题，我们提出了一个与实例一致的隐式函数（INSTPIFU）进行详细的对象重建。与实例对准的注意模块结合使用，我们的方法有权将混合的局部特征与被遮挡的实例相结合。此外，与以前的方法不同，该方法仅表示房间背景为3D边界框，深度图或一组平面，我们通过隐式表示恢复了背景的细节。在Sun RGB-D，Pix3D，3D-Future和3D-Front数据集上进行的广泛实验表明，我们的方法在背景和前景对象重建中都优于现有方法。我们的代码和模型将公开可用。
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D shapes of indoor objects with limited geometry quality because of the heavy occlusion of indoor scenes. To solve this, we propose an instance-aligned implicit function (InstPIFu) for detailed object reconstruction. Combining with instance-aligned attention module, our method is empowered to decouple mixed local features toward the occluded instances. Additionally, unlike previous methods that simply represents the room background as a 3D bounding box, depth map or a set of planes, we recover the fine geometry of the background via implicit representation. Extensive experiments on the SUN RGB-D, Pix3D, 3D-FUTURE, and 3D-FRONT datasets demonstrate that our method outperforms existing approaches in both background and foreground object reconstruction. Our code and model will be made publicly available.
