  Previous top-performing approaches for point cloud instance segmentation
involve a bottom-up strategy, which often includes inefficient operations or
complex pipelines, such as grouping over-segmented components, introducing
additional steps for refining, or designing complicated loss functions. The
inevitable variation in the instance scales can lead bottom-up methods to
become particularly sensitive to hyper-parameter values. To this end, we
propose instead a dynamic, proposal-free, data-driven approach that generates
the appropriate convolution kernels to apply in response to the nature of the
instances. To make the kernels discriminative, we explore a large context by
gathering homogeneous points that share identical semantic categories and have
close votes for the geometric centroids. Instances are then decoded by several
simple convolutional layers. Due to the limited receptive field introduced by
the sparse convolution, a small light-weight transformer is also devised to
capture the long-range dependencies and high-level interactions among point
samples. The proposed method achieves promising results on both ScanetNetV2 and
S3DIS, and this performance is robust to the particular hyper-parameter values
chosen. It also improves inference speed by more than 25% over the current
state-of-the-art. Code is available at: https://git.io/DyCo3D
