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Deep neural networks designed for point clouds, also called point cloud neural networks (PCNNs), are attracting increasing attention in recent years. In this work, we propose the first model compression framework referred to as JointPruning (JP) that is specifically designed for compressing PCNNs. Observing that the redundancies in PCNNs are largely affected by certain parameters like the number of points, we first propose a new search space specifically designed for PCNNs. By searching the optimal pruning policy in our newly proposed search space, our JP framework can prune the PCNNs at different levels and simultaneously reduce the redundancies along multiple dimensions. As the newly proposed search space consists of multiple levels and the policy value at each level is continuous in our JP framework, it is hard to directly search for the best pruning policy in such a large search space. To this end, we further propose two strategies called search space refinement and validation set extension to progressively refine the granularity of our searching process in a coarse-to-fine and easy-to-hard fashion, which can help us gradually find better pruning policies. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our JP framework for compressing PCNNs. [ABSTRACT FROM AUTHOR]

Voting-based methods (e.g., VoteNet) have achieved promising results for 3D object detection. However, the simple voting operation in VoteNet may lead to less accurate voting results that are far away from the true object centers. In this work, we propose a simple but effective 3D object detection method called Transformer3D-Det (T3D), in which we additionally introduce a transformer based vote refinement module to refine the voting results of VoteNet and can thus significantly improve the 3D object detection performance. Specifically, our T3D framework consists of three modules: a vote generation module, a vote refinement module, and a bounding box generation module. Given an input point cloud, we first utilize the vote generation module to generate multiple coarse vote clusters. Then, the clustered coarse votes will be refined by using our transformer based vote refinement module to produce more accurate and meaningful votes. Finally, the bounding box generation module takes the refined vote clusters as the input and generates the final detection result for the input point cloud. To alleviate the impact of inaccurate votes, we also propose a new non-vote loss function to train our T3D. As a result, our T3D framework can achieve better 3D object detection performance. Comprehensive experiments on two benchmark datasets ScanNetV2 and SUN RGB-D demonstrate the effectiveness of our T3D framework for 3D object detection. [ABSTRACT FROM AUTHOR]

In this work, we propose a simple but effective channel pruning framework called Progressive Channel Pruning (PCP) to accelerate Convolutional Neural Networks (CNNs). In contrast to the existing channel pruning methods that prune the channels only once per layer in a layer-by-layer fashion, our new progressive framework iteratively prunes a small number of channels from several selected layers, which consists of a three-step attempting-selecting-pruning pipeline in each iteration. In the attempting step, we attempt to prune a pre-defined number of channels from one layer by using any existing channel pruning methods and estimate the accuracy drop for this layer based on the labelled samples in the validation set. In the selecting step, based on the estimated accuracy drops for all layers, we propose a greedy strategy to automatically select a set of layers that will lead to less overall accuracy drop after pruning these layers. In the pruning step, we prune a small number of channels from these selected layers. We further extend our PCP framework to prune channels for the deep transfer learning methods like Domain Adversarial Neural Network (DANN), in which we effectively reduce the data distribution mismatch in the channel pruning process by using both labelled samples from the source domain and pseudo-labelled samples from the target domain. Our comprehensive experiments on two benchmark datasets demonstrate that our PCP framework outperforms the existing channel pruning approaches under both supervised learning and transfer learning settings. [ABSTRACT FROM AUTHOR]