You are all cordially invited to the AMLab seminar on Thursday November 22 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Maurice Weiler will give a talk titled “3D Steerable CNNs”. Afterwards there are the usual drinks and snacks.
Abstract: We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.