
The paper proposes efficient FPGA accelerator cores, PointLKCore and ReAgentCore, for deep learning-based point cloud registration methods that avoid costly feature matching.


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efficient-fpga-accelerated-point-cloud-registration-with-pointnet-features


Efficient FPGA-Accelerated Point Cloud Registration with PointNet Features


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The authors propose a method to reconstruct point clouds from few images and denoise point clouds from their rendering by exploiting prior knowledge distilled from image-based deep learning models.


efficient-point-cloud-reconstruction-and-denoising-via-learned-gaussian-splats-and-fine-tuned-diffusion-features


Efficient Point Cloud Reconstruction and Denoising via Learned Gaussian Splats and Fine-Tuned Diffusion Features



Our approach directly matches superpoints between input point clouds to robustly estimate the SE(3) transformation matrix, without relying on cumbersome post-processing steps.


a-robust-and-efficient-baseline-for-point-cloud-registration-via-direct-superpoint-matching


A Robust and Efficient Baseline for Point Cloud Registration via Direct Superpoint Matching



スケーリング原則に基づいて、Point Transformer V3（PTv3）は単純で効率的なモデルを提供し、強力なパフォーマンスを実現します。


point-transformer-v3-simpler-faster-stronger


Point Transformer V3: Simpler, Faster, Stronger
