Max Robotics — Global Service Robot Directory + US Certification

About

Min Sun is a prominent computer vision researcher whose work spans 3D scene understanding, visual navigation, and deep learning-based perception systems. His research career demonstrates a consistent focus on extracting rich spatial information from visual inputs, beginning with his influential 2010 work on Depth-Encoded Hough Voting for joint object detection and shape recovery, which garnered 150 citations and established foundational techniques for simultaneous object recognition and 3D reconstruction. Sun has made significant contributions to omnidirectional vision, developing the novel O-CNN (Omnidirectional Convolutional Neural Network) architecture for visual place recognition under challenging camera pose variations, a paper that accumulated over 74 citations. His work on indoor scene understanding is further demonstrated through Flat2Layout, which broke new ground by estimating layouts for general room types beyond conventional box-shaped assumptions, and the LayoutMP3D dataset, which advances panoramic 3D layout estimation for robotics and virtual reality applications. More recently, Sun has pushed into generative 3D modeling, exploring Gaussian Splatting combined with hybrid diffusion priors for single-image 3D generation. Across video captioning, navigation, and geometric reconstruction, his body of work reflects a sustained commitment to enabling machines to perceive and interact with three-dimensional environments intelligently.