Decoupling Makes Weakly Supervised Local Feature Better

Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supervision cannot distinguish the losses caused by the detection and description steps, directly conducting weakly supervised learning within a joint describe-then-detect pipeline suffers limited performance. In this paper, we propose a decoupled describe-then-detect pipeline tailored for weakly supervised local feature learning. Within our pipeline, the detection step is decoupled from the description step and postponed until discriminative and robust descriptors are learned. In addition, we introduce a line-to-window search strategy to explicitly use the camera pose information for better descriptor learning. Extensive experiments show that our method, namely PoSFeat (Camera Pose Supervised Feature), outperforms previous fully and weakly supervised methods and achieves state-of-the-art performance on a wide range of downstream tasks.

Distortion-aware Monocular Depth Estimation for Omnidirectional Images

A main challenge for tasks on panorama lies in the distortion of objects among images. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) dense depth estimation network to address this challenge on indoor panoramas with two steps. First, we introduce a distortion-aware module to extract calibrated semantic features from omnidirectional images. Specifically, we exploit deformable convolution to adjust its sampling grids to geometric variations of distorted objects on panoramas and then utilize a strip pooling module to sample against horizontal distortion introduced by inverse gnomonic projection. Second, we further introduce a plug-and-play spherical-aware weight matrix for our objective function to handle the uneven distribution of areas projected from a sphere. Experiments on the 360D dataset show that the proposed method can effectively extract semantic features from distorted panoramas and alleviate the supervision bias caused by distortion. It achieves state-of-the-art performance on the 360D dataset with high efficiency.

Learning Local Features with Context Aggregation for Visual Localization

Keypoint detection and description is fundamental yet important in many vision applications. Most existing methods use detect-then-describe or detect-and-describe strategy to learn local features without considering their context information. Consequently, it is challenging for these methods to learn robust local features. In this paper, we focus on the fusion of low-level textual information and high-level semantic context information to improve the discrimitiveness of local features. Specifically, we first estimate a score map to represent the distribution of potential keypoints according to the quality of descriptors of all pixels. Then, we extract and aggregate multi-scale high-level semantic features based by the guidance of the score map. Finally, the low-level local features and high-level semantic features are fused and refined using a residual module. Experiments on the challenging local feature benchmark dataset demonstrate that our method achieves the state-of-the-art performance in the local feature challenge of the visual localization benchmark.