RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds

• URL: http://arxiv.org/abs/2503.12382v1
• Date: Sun, 16 Mar 2025 07:03:12 GMT
• Title: RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds
• Abstract summary: This paper proposes RENO, the first real-time neural for 3D LiDAR point clouds, achieving superior performance with a lightweight model.<n> Experimental results demonstrate that the proposed RENO achieves real-time coding speed, 10 fps at 14-bit depth on a desktop platform.
• Score: 30.462630510010026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the substantial advancements demonstrated by learning-based neural models in the LiDAR Point Cloud Compression (LPCC) task, realizing real-time compression - an indispensable criterion for numerous industrial applications - remains a formidable challenge. This paper proposes RENO, the first real-time neural codec for 3D LiDAR point clouds, achieving superior performance with a lightweight model. RENO skips the octree construction and directly builds upon the multiscale sparse tensor representation. Instead of the multi-stage inferring, RENO devises sparse occupancy codes, which exploit cross-scale correlation and derive voxels' occupancy in a one-shot manner, greatly saving processing time. Experimental results demonstrate that the proposed RENO achieves real-time coding speed, 10 fps at 14-bit depth on a desktop platform (e.g., one RTX 3090 GPU) for both encoding and decoding processes, while providing 12.25% and 48.34% bit-rate savings compared to G-PCCv23 and Draco, respectively, at a similar quality. RENO model size is merely 1MB, making it attractive for practical applications. The source code is available at https://github.com/NJUVISION/RENO.

Related papers

• Towards Practical Lossless Neural Compression for LiDAR Point Clouds [84.36825469211375]
  extreme sparsity of high-precision geometric details hinders efficient context modeling.<n>Our framework comprises two lightweight modules.<n>Experiments demonstrate competitive compression performance at real-time speed.
  arXiv  Detail & Related papers  (2026-03-26T10:02:07Z)
• Ultra-lightweight Neural Video Representation Compression [18.390031994984096]
  We propose NVRC-Lite, which incorporates two key changes to neural video representation.<n>Firstly, we integrated multi-scale feature grids into our lightweight neural representation.<n> Secondly, we address the issue that existing INRs typically leverage autoregressive models for entropy coding.<n>Our experimental results demonstrate that NVRC-Lite outperforms C3, one of the best lightweight INR-based video codecs.
  arXiv  Detail & Related papers  (2025-12-03T17:56:44Z)
• Rapid Salient Object Detection with Difference Convolutional Neural Networks [49.838283141381716]
  This paper addresses the challenge of deploying salient object detection (SOD) on resource-constrained devices with real-time performance.<n>We propose an efficient network design that combines traditional wisdom on SOD and the representation power of modern CNNs.
  arXiv  Detail & Related papers  (2025-07-01T20:41:05Z)
• 70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float [71.43026659686679]
  Large Language Models (LLMs) have grown rapidly in size, creating challenges for efficient deployment on resource-constrained hardware. We introduce Dynamic-Length Float (DFloat11), a compression framework that reduces LLM size by 30% while preserving outputs that are bit-for-bit identical to the original model.
  arXiv  Detail & Related papers  (2025-04-15T22:38:38Z)
• Representing 3D Shapes With 64 Latent Vectors for 3D Diffusion Models [21.97308739556984]
  COD-VAE encodes 3D shapes into a COmpact set of 1D latent vectors without sacrificing quality. COD-VAE achieves 16x compression compared to the baseline while maintaining quality. This enables 20.8x speedup in generation, highlighting that a large number of latent vectors is not a prerequisite for high-quality reconstruction and generation.
  arXiv  Detail & Related papers  (2025-03-11T06:29:39Z)
• Efficient 3D Recognition with Event-driven Spike Sparse Convolution [15.20476631850388]
  Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D-temporal features.<n>We introduce the Spike Voxel Coding (SVC) scheme, which encodes the 3D point clouds into a sparse spike train space.<n>We propose a Spike Sparse Convolution (SSC) model for efficiently extracting 3D sparse point cloud features.
  arXiv  Detail & Related papers  (2024-12-10T09:55:15Z)
• ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model [77.71796503321632]
  We introduce a context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS. Our work pioneers the context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS and 15 times compared to the most recent state-of-the-art work Scaffold-GS.
  arXiv  Detail & Related papers  (2024-05-31T09:23:39Z)
• Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation [7.539498729072623]
  Implicit Neural Representation (INR) is an innovative approach for representing complex shapes or objects without explicitly defining their geometry or surface structure. Previous research has demonstrated the effectiveness of using neural networks as INR for image compression, showcasing comparable performance to traditional methods such as JPEG. This paper introduces Rapid-INR, a novel approach that utilizes INR for encoding and compressing images, thereby accelerating neural network training in computer vision tasks.
  arXiv  Detail & Related papers  (2023-06-29T05:49:07Z)
• Scalable Neural Video Representations with Learnable Positional Features [73.51591757726493]
  We show how to train neural representations with learnable positional features (NVP) that effectively amortize a video as latent codes. We demonstrate the superiority of NVP on the popular UVG benchmark; compared with prior arts, NVP not only trains 2 times faster (less than 5 minutes) but also exceeds their encoding quality as 34.07rightarrow$34.57 (measured with the PSNR metric)
  arXiv  Detail & Related papers  (2022-10-13T08:15:08Z)
• NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
  This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction. The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network. A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
  arXiv  Detail & Related papers  (2022-09-29T04:06:00Z)
• Lightweight compression of neural network feature tensors for collaborative intelligence [32.03465747357384]
  In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a relatively low-complexity device such as a mobile phone or edge device. This paper presents a novel lightweight compression technique designed specifically to code the activations of a split DNN layer.
  arXiv  Detail & Related papers  (2021-05-12T23:41:35Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression [77.8842824702423]
  We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the memory footprint. Our algorithm can be used to reduce the onboard and offboard storage of LiDAR points for applications such as self-driving cars.
  arXiv  Detail & Related papers  (2020-05-14T17:48:49Z)
• REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild [62.36144064259933]
  We propose REST, a new method that simultaneously tackles both issues via adversarial training and controlling the Lipschitz constant of the neural network. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference.
  arXiv  Detail & Related papers  (2020-01-29T17:23:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.