arXiv Machine Learning August 10, 2026 By Niclas Meyer, Stefan Reitmann

Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edge

Read the original on arXiv Machine Learning →

arXiv:2608. 07106v1 Announce Type: new Abstract: Deploying three-dimensional deep learning frameworks to low-power embedded processors is bottlenecked by the unstructured nature of spatial data and the resource-intensive distance sorting algorithms often used before neural network inference.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

Jul 10

On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection

arXiv:2607. 08391v1 Announce Type: cross Abstract: Making tradeoffs between execution latency and result utility (i.

By Ahmet Soyyigit, Shuochao Yao, Heechul Yun

agents computer-vision robotics

arXiv AI

Jun 18

A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation

arXiv:2605. 17131v2 Announce Type: replace-cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity.

By Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran

computer-vision benchmarks

arXiv AI

Jun 26

Learning Motion Feasibility from Point Clouds in Cluttered Environments

arXiv:2606. 26700v1 Announce Type: cross Abstract: Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation.

By Sajid Ansari, Arthi, Girish Varma, Antony Thomas

llms robotics benchmarks

arXiv AI

Jul 31

FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

arXiv:2607. 26645v1 Announce Type: cross Abstract: Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion.

By Wenzhe He, Meng Wang, JiaWei Qian, Jinfeng Xu, Ying Liu, Ruihui Li

efficiency benchmarks safety

arXiv Machine Learning

Jul 9

Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

arXiv:2607. 06922v1 Announce Type: new Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers.

By Shuo Huai, Di Liu, Hao Kong, Weichen Liu, Ravi Subramaniam, Christian Makaya, Qian Lin

efficiency benchmarks safety

arXiv Machine Learning

6d ago

Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines

arXiv:2608. 11770v1 Announce Type: cross Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis.

By Vaishnav Raju

agents robotics efficiency

Related stories

On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection

A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation

Learning Motion Feasibility from Point Clouds in Cluttered Environments

FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines