  We propose an approach to instance segmentation from 3D point clouds based on
dynamic convolution. This enables it to adapt, at inference, to varying feature
and object scales. Doing so avoids some pitfalls of bottom up approaches,
including a dependence on hyper-parameter tuning and heuristic post-processing
pipelines to compensate for the inevitable variability in object sizes, even
within a single scene. The representation capability of the network is greatly
improved by gathering homogeneous points that have identical semantic
categories and close votes for the geometric centroids. Instances are then
decoded via several simple convolution layers, where the parameters are
generated conditioned on the input. The proposed approach is proposal-free, and
instead exploits a convolution process that adapts to the spatial and semantic
characteristics of each instance. A light-weight transformer, built on the
bottleneck layer, allows the model to capture long-range dependencies, with
limited computational overhead. The result is a simple, efficient, and robust
approach that yields strong performance on various datasets: ScanNetV2, S3DIS,
and PartNet. The consistent improvements on both voxel- and point-based
architectures imply the effectiveness of the proposed method. Code is available
at: https://git.io/DyCo3D
