Weixin Mao

Papers

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About

Dr. Weixin Mao is a rising researcher in computer vision, with a sharp focus on 3D object detection for autonomous driving and robotics. His most cited work, "PersDet: Monocular 3D Detection in Perspective Bird's-Eye-View" (2022), tackles a critical bottleneck in deploying 3D detectors on edge devices. While Bird's-Eye-View (BEV) methods offer superior performance, they typically rely on specialized feature-sampling operators unsupported by many edge platforms. Mao’s key contribution is a novel perspective-based BEV representation that eliminates the need for these operators, enabling efficient, high-quality 3D detection directly from monocular images. This work bridges the gap between state-of-the-art accuracy and real-world deployability, making autonomous systems more practical. Though early in his career, his research demonstrates a clear impact—his paper has already garnered 3 citations—and addresses a fundamental challenge in vision-based perception. Mao’s work is particularly notable for its practical orientation, aiming to bring cutting-edge 3D detection to resource-constrained environments, a crucial step toward safer, more accessible autonomous vehicles and robots.