Point Transformer V3: Simpler, Faster, Stronger

TL;DR

Point Transformer V3 (PTv3) rethinks 3D point cloud backbones through a scaling-first philosophy, trading intricate, inefficient local mechanisms for serialized point representations that expand the receptive field from 16 to 1024 while boosting speed and reducing memory usage. By employing space-filling curve-based serialization, patch-based attention, and an enhanced positional encoding (xCPE), PTv3 achieves state-of-the-art results across over 20 indoor and outdoor tasks, especially when combined with multi-dataset joint training (PPT). The approach demonstrates that scalability can unlock stronger performance with simpler components, enabling practical, large-scale 3D representation learning. Limitations and future directions include optimizing attention mechanisms further and exploring broader multimodal pre-training.

Abstract

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.

Figures (6)

• Figure 1: Overview of Point Transformer V3 (PTv3). Compared to its predecessor, PTv2 wu2022point, our PTv3 shows superiority in the following aspects: 1. Stronger performance. PTv3 achieves state-of-the-art results across a variety of indoor and outdoor 3D perception tasks. 2. Wider receptive field. Benefit from the simplicity and efficiency, PTv3 expands the receptive field from 16 to 1024 points. 3. Faster speed. PTv3 significantly increases processing speed, making it suitable for latency-sensitive applications. 4. Lower Memory Consumption. PTv3 reduces memory usage, enhancing accessibility for broader situations.
• Figure 2: Latency treemap of each components of PTv2. We benchmark and visualize the proportion of the forward time of each component of PTv2. KNN Query and RPE occupy a total of 54% of forward time.
• Figure 3: Point cloud serialization. We show the four patterns of serialization with a triplet visualization. For each triplet, we show the space-filling curve for serialization (left), point cloud serialization var sorting order within the space-filling curve (middle), and grouped patches of the serialized point cloud for local attention (right). Shifting across the four serialization patterns allows the attention mechanism to capture various spatial relationships and contexts, leading to an improvement in model accuracy and generalization capacity.
• Figure 4: Patch grouping. (a) Reordering point cloud according to order derived from a specific serialization pattern. (b) Padding point cloud sequence by borrowing points from neighboring patches to ensure it is divisible by the designated patch size.
• Figure 5: Patch interaction. (a) Standard patch grouping with a regular, non-shifted arrangement; (b) Shift Dilation where points are grouped at regular intervals, creating a dilated effect; (c) Shift Patch, which applies a shifting mechanism similar to the shift window approach; (d) Shift Order where different serialization patterns are cyclically assigned to successive attention layers; (d) Shuffle Order, where the sequence of serialization patterns is randomized before being fed to attention layers.