Kavita Bala Research Group

Cornell University

How can visual systems identify materials and lighting from photographs to enable realistic editing and recognition? The group develops datasets and algorithms for material recognition and visual search, including the Materials in Context Database and visual similarity models. Researchers build physically-based and differentiable rendering methods for recovering shape and material properties to support editing and relighting. The group applies remote-sensing and scale-aware recognition techniques to satellite imagery and cloud removal benchmarks for Earth-observation tasks. Methods combine convolutional neural networks, differentiable Monte Carlo rendering, and dataset/benchmark construction to evaluate recognition and inverse-rendering performance.