  This paper is not motivated to seek innovation within the attention
mechanism. Instead, it focuses on overcoming the existing trade-offs between
accuracy and efficiency within the context of point cloud processing,
leveraging the power of scale. Drawing inspiration from recent advances in 3D
large-scale representation learning, we recognize that model performance is
more influenced by scale than by intricate design. Therefore, we present Point
Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the
accuracy of certain mechanisms that are minor to the overall performance after
scaling, such as replacing the precise neighbor search by KNN with an efficient
serialized neighbor mapping of point clouds organized with specific patterns.
This principle enables significant scaling, expanding the receptive field from
16 to 1024 points while remaining efficient (a 3x increase in processing speed
and a 10x improvement in memory efficiency compared with its predecessor,
PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that
span both indoor and outdoor scenarios. Further enhanced with multi-dataset
joint training, PTv3 pushes these results to a higher level.
