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The paper presents a simple and effective learning-based method for computinga discriminative 3D point cloud descriptor for place recognition purposes.Recent state-of-the-art methods have relatively complex architectures such asmulti-scale oyramid of point Transformers combined with a pyramid of featureaggregation modules. Our method uses a simple and efficient 3D convolutionalfeature extraction, based on a sparse voxelized representation, enhanced withchannel attention blocks. We employ recent advances in image retrieval andpropose a modified version of a loss function based on a differentiable averageprecision approximation. Such loss function requires training with very largebatches for the best results. This is enabled by using multistagedbackpropagation. Experimental evaluation on the popular benchmarks proves theeffectiveness of our approach, with a consistent improvement over the state ofthe art

Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training