  3D object detection is an essential vision technique for various robotic
systems, such as augmented reality and domestic robots. Transformers as
versatile network architectures have recently seen great success in 3D point
cloud object detection. However, the lack of hierarchy in a plain transformer
restrains its ability to learn features at different scales. Such limitation
makes transformer detectors perform worse on smaller objects and affects their
reliability in indoor environments where small objects are the majority. This
work proposes two novel attention operations as generic hierarchical designs
for point-based transformer detectors. First, we propose Aggregated Multi-Scale
Attention (MS-A) that builds multi-scale tokens from a single-scale input
feature to enable more fine-grained feature learning. Second, we propose
Size-Adaptive Local Attention (Local-A) with adaptive attention regions for
localized feature aggregation within bounding box proposals. Both attention
operations are model-agnostic network modules that can be plugged into existing
point cloud transformers for end-to-end training. We evaluate our method on two
widely used indoor detection benchmarks. By plugging our proposed modules into
the state-of-the-art transformer-based 3D detectors, we improve the previous
best results on both benchmarks, with more significant improvements on smaller
objects.
