GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields

Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitigate these imperfections from various sources with a joint solution: we take advantage of the ability of generative adversarial networks (GANs) to produce realistic images and use them to enhance realism in 3D scene reconstruction with NeRFs. To this end, we learn the patch distribution of a scene using an adversarial discriminator, which provides feedback to the radiance field reconstruction, thus improving realism in a 3D-consistent fashion. Thereby, rendering artifacts are repaired directly in the underlying 3D representation by imposing multi-view path rendering constraints. In addition, we condition a generator with multi-resolution NeRF renderings which is adversarially trained to further improve rendering quality. We demonstrate that our approach significantly improves rendering quality, e.g., nearly halving LPIPS scores compared to Nerfacto while at the same time improving PSNR by 1.4dB on the advanced indoor scenes of Tanks and Temples.

End2End Multi-View Feature Matching using Differentiable Pose Optimization

Learning-based approaches have become indispensable for camera pose estimation. However, feature detection, description, matching, and pose optimization are often approached in an isolated fashion. In particular, erroneous feature matches have severe impact on subsequent camera pose estimation and often require additional measures such as outlier rejection. Our method tackles this challenge by addressing feature matching and pose optimization jointly: first, we integrate information from multiple views into the matching by spanning a graph attention network across multiple frames to predict their matches all at once. Second, the resulting matches along with their predicted confidences are used for robust pose optimization with a differentiable Gauss-Newton solver. End-to-end training combined with multi-view feature matching boosts the pose estimation metrics compared to SuperGlue by 8.9% on ScanNet and 10.7% on MegaDepth on average. Our approach improves both pose estimation and matching accuracy over state-of-the-art matching networks. Training feature matching across multiple views with gradients from pose optimization naturally learns to disregard outliers, thereby rendering additional outlier handling unnecessary, which is highly desirable for pose estimation systems.

Dense Depth Priors for Neural Radiance Fields from Sparse Input Views

Neural radiance fields (NeRF) encode a scene into a neural representation that enables photo-realistic rendering of novel views. However, a successful reconstruction from RGB images requires a large number of input views taken under static conditions - typically up to a few hundred images for room-size scenes. Our method aims to synthesize novel views of whole rooms from an order of magnitude fewer images. To this end, we leverage dense depth priors in order to constrain the NeRF optimization. First, we take advantage of the sparse depth data that is freely available from the structure from motion (SfM) preprocessing step used to estimate camera poses. Second, we use depth completion to convert these sparse points into dense depth maps and uncertainty estimates, which are used to guide NeRF optimization. Our method enables data-efficient novel view synthesis on challenging indoor scenes, using as few as 18 images for an entire scene.