  Estimating surface reflectance (BRDF) is one key component for complete 3D
scene capture, with wide applications in virtual reality, augmented reality,
and human computer interaction. Prior work is either limited to controlled
environments (\eg gonioreflectometers, light stages, or multi-camera domes), or
requires the joint optimization of shape, illumination, and reflectance, which
is often computationally too expensive (\eg hours of running time) for
real-time applications. Moreover, most prior work requires HDR images as input
which further complicates the capture process. In this paper, we propose a
lightweight approach for surface reflectance estimation directly from $8$-bit
RGB images in real-time, which can be easily plugged into any 3D
scanning-and-fusion system with a commodity RGBD sensor. Our method is
learning-based, with an inference time of less than 90ms per scene and a model
size of less than 340K bytes. We propose two novel network architectures,
HemiCNN and Grouplet, to deal with the unstructured input data from multiple
viewpoints under unknown illumination. We further design a loss function to
resolve the color-constancy and scale ambiguity. In addition, we have created a
large synthetic dataset, SynBRDF, which comprises a total of $500$K RGBD images
rendered with a physically-based ray tracer under a variety of natural
illumination, covering $5000$ materials and $5000$ shapes. SynBRDF is the first
large-scale benchmark dataset for reflectance estimation. Experiments on both
synthetic data and real data show that the proposed method effectively recovers
surface reflectance, and outperforms prior work for reflectance estimation in
uncontrolled environments.
