Point Transformer V3: Simpler Faster Stronger
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.
Code (1)
Tasks
Representation LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection
Single point supervised oriented object detection has gained attention and made initial progress within the community. Diverse from those approaches relying on one-shot samples or powerful pretrained models (e.g. SAM), P…
object-detectionObject DetectionOriented Object DetectionResT V2: Simpler, Faster and Stronger
This paper proposes ResTv2, a simpler, faster, and stronger multi-scale vision Transformer for visual recognition. ResTv2 simplifies the EMSA structure in ResTv1 (i.e., eliminating the multi-head interaction part) and em…
Semantic SegmentationPoint Transformer V3: Simpler, Faster, Stronger
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, lever…
3D Semantic SegmentationLIDAR Semantic SegmentationRepresentation LearningSemantic SegmentationEfficient Linear Attention for Fast and Accurate Keypoint Matching
Recently Transformers have provided state-of-the-art performance in sparse matching, crucial to realize high-performance 3D vision applications. Yet, these Transformers lack efficiency due to the quadratic computational …
Image MatchingVisual LocalizationLitePT: Lighter Yet Stronger Point Transformer
Modern neural architectures for 3D point cloud processing contain both convolutional layers and attention blocks, but the best way to assemble them remains unclear. We analyse the role of different computational blocks i…