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.
Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edge
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.
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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.
arXiv Computer Vision
Sep 16
SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion
arXiv:2609.17413v1 Announce Type: new Abstract: This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring compl...
arXiv Computer Vision
Sep 10
Geometry Without Coordinates: LiDAR Diffusion as a 3D Feature Bridge
arXiv:2609.10322v1 Announce Type: new Abstract: Transferring the rich priors of large 2D foundation models to sparse 3D LiDAR remains challenging, as training native 3D foundation models at comparabl...
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.
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.
arXiv AI
Sep 12
Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless
The paper introduces a lightweight LiDAR-only perception pipeline for Formula Student Driverless vehicles that runs entirely on CPU. It combines ground removal, IMU-based motion compensation, DBSCAN clustering, and a Random Forest classifier, reducing the feature set from 12 to 7 while maintaining high accuracy. On a dataset of 2,371 labeled clusters, the system achieves an F1-score of 98.33% with an end-to-end runtime of 3.13 ms.
arXiv AI
Sep 16
RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
arXiv:2508.19003v2 Announce Type: replace-cross Abstract: Roof plane segmentation is one of the key procedures for reconstructing three-dimensional (3D) building models at levels of detail (LoD) 2 an...
arXiv Computer Vision
Sep 15
Unsupervised Point Cloud Registration via Training-Time Semantic Guidance
arXiv:2609.15228v1 Announce Type: new Abstract: Unsupervised registration of large-scale LiDAR point clouds remains challenging due to the geometric ambiguity inherent in outdoor scenes, which degrad...
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.
arXiv Machine Learning
Aug 13
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.
arXiv Computer Vision
Sep 25
M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera--LiDAR Novel View Synthesis
M3GD introduces a multimodal representation that fuses pre‑trained 2D image and 3D LiDAR foundation models for robotic novel view synthesis, avoiding the need for a separate cross‑modal translator. By projecting LiDAR onto the image latent grid and injecting the resulting geometry‑aware packets via a lightweight residual adapter, the method enhances both RGB and depth synthesis on the GrandTour dataset compared to an image‑only baseline. Ablation studies confirm that pixel‑aligned LiDAR content drives the performance gains, and real‑world deployment on a ground robot demonstrates a tunable quality–cost trade‑off.