BevSplat: Resolving Height Ambiguity via Feature-Based Gaussian Primitives for Weakly-Supervised Cross-View Localization

This paper addresses the problem of weakly supervised cross-view localization, where the goal is to estimate the pose of a ground camera relative to a satellite image with noisy ground truth annotations. A common approach to bridge the cross-view domain gap for pose estimation is Bird's-Eye View (BEV) synthesis. However, existing methods struggle with height ambiguity due to the lack of depth information in ground images and satellite height maps. Previous solutions either assume a flat ground plane or rely on complex models, such as cross-view transformers. We propose BevSplat, a novel method that resolves height ambiguity by using feature-based Gaussian primitives. Each pixel in the ground image is represented by a 3D Gaussian with semantic and spatial features, which are synthesized into a BEV feature map for relative pose estimation. Additionally, to address challenges with panoramic query images, we introduce an icosphere-based supervision strategy for the Gaussian primitives. We validate our method on the widely used KITTI and VIGOR datasets, which include both pinhole and panoramic query images. Experimental results show that BevSplat significantly improves localization accuracy over prior approaches.

Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control

Generating street-view images from satellite imagery is a challenging task, particularly in maintaining accurate pose alignment and incorporating diverse environmental conditions. While diffusion models have shown promise in generative tasks, their ability to maintain strict pose alignment throughout the diffusion process is limited. In this paper, we propose a novel Iterative Homography Adjustment (IHA) scheme applied during the denoising process, which effectively addresses pose misalignment and ensures spatial consistency in the generated street-view images. Additionally, currently, available datasets for satellite-to-street-view generation are limited in their diversity of illumination and weather conditions, thereby restricting the generalizability of the generated outputs. To mitigate this, we introduce a text-guided illumination and weather-controlled sampling strategy that enables fine-grained control over the environmental factors. Extensive quantitative and qualitative evaluations demonstrate that our approach significantly improves pose accuracy and enhances the diversity and realism of generated street-view images, setting a new benchmark for satellite-to-street-view generation tasks.

Double-decker: Productive Backscatter Communication Using a Single Commodity Receiver

Backscatter communication has attracted significant attention for Internet-of-Things applications due to its ultra-low-power consumption. The state-of-the-art backscatter systems no longer require dedicated carrier generators and leverage ambient signals as carriers. However, there is an emerging challenge: most prior systems need dual receivers to capture the original and backscattered signals at the same time for tag data demodulation. This is not conducive to the widespread deployment of backscatter communication. To address this problem, we present double-decker, a novel backscatter system that only requires a single commercial device for backscatter communication. The key technology of double-decker is to divide the carrier OFDM symbols into two parts, which are pilot symbols and data symbols. Pilot symbols can be used as reference signals for tag data demodulation, thus getting rid of the dependence on the dual receiver structure. We have built an FPGA prototype and conducted extensive experiments. Empirical results show that when the excitation signal is 802.11g, double-decker achieves a tag data rate of 35.2kbps and a productive data rate of 38kbps, respectively. The communication range of double-decker is up to 28m in LOS deployment and 24m in NLOS deployment.