SparseRefine is a novel approach that enhances dense low-resolution predictions with sparse high-resolution refinements. It achieves significant speedup: 1.5 to 3.7 times when applied to HRNet-W48, SegFormer-B5, Mask2Former-T/L and SegNeXt-L on Cityscapes, with negligible to no loss of accuracy.

TorchSparse++ is a high-performance computing library for efficient 3D sparse convolution. It offers significant performance improvement over TorchSparse++ by overlapping computation with memory access. It also searches for the best execution strategy for sparse workloads within a large design space through auto-tuning.

EfficientViT is a new family of vision models for high-resolution dense prediction. It achieves global receptive field and multi-scale learning with only hardware-efficient operations. EfficientViT delivers remarkable performance gains over previous models with speedup on diverse hardware platforms, including mobile CPU, edge GPU, and cloud GPU.

We present FlatFormer, an efficient ViT architecture for large-scale point cloud analysis.

BEVFusion unifies multi-modal features in the shared bird’s-eye view (BEV) representation space, which nicely preserves both geometric and semantic information. It establishes the new state of the art on nuScenes, achieving 1.3% higher mAP and NDS on 3D object detection and 13.6% higher mIoU on BEV map segmentation with 1.9x lower computation cost.

TorchSparse is a high-performance computing library for efficient 3D sparse convolution. This library aims at accelerating sparse computation in 3D, in particular the Sparse Convolution operation.