OpenNovelty.org

Low-Latency Neural LiDAR Compression with 2D Context Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Data Compression
Abstract:

Context modeling is fundamental to LiDAR point cloud compression. Existing methods rely on computationally intensive 3D contexts, such as voxel and octree, which struggle to balance the compression efficiency and coding speed. In this work, we propose a neural LiDAR compressor based on 2D context models that simultaneously supports high-efficiency compression, fast coding, and universal geometry-intensity compression. The 2D context structure significantly reduces the coding latency. We further develop a comprehensive context model that integrates spatial latents, temporal references, and cross-modal camera context in the 2D domain to enhance the compression performance. Specifically, we first represent the point cloud as a range image and propose a multi-scale spatial context model to capture the intra-frame dependencies. Furthermore, we design an optical-flow-based temporal context model for inter-frame prediction. Moreover, we incorporate a deformable attention module and a context refinement strategy to predict LiDAR scans from camera images. In addition, we develop a backbone for joint geometry and intensity compression, which unifies the compression of both modalities while minimizing redundant computation. Experiments demonstrate significant improvements in both rate-distortion performance and coding speed. The code will be released upon the acceptance of the paper.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a neural LiDAR compressor using 2D context models for joint geometry-intensity compression, emphasizing fast coding speed alongside high compression efficiency. It resides in the Real-Time and Lightweight Neural Compression leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader deep learning-based compression landscape. This leaf focuses specifically on methods optimized for low-latency processing with lightweight architectures, distinguishing it from heavier neural approaches that prioritize compression ratio over speed.

The taxonomy reveals that the paper's immediate neighbors include works like Reno and FLiCR, which similarly target real-time performance but may employ different network backbones or entropy coding strategies. Broader sibling branches include Autoencoder and Latent Representation methods, Entropy Modeling approaches, and Recurrent/Temporal Neural Networks, all under the Deep Learning-Based Compression umbrella. The paper's use of 2D range image representations also connects it to the Image-Based and 2D Projection Representations branch, while its temporal context model relates to the Temporal Prediction and Inter-Frame Coding subtopic, showing cross-cutting ties across multiple taxonomy branches.

Among fourteen candidates examined, the first contribution (2D context model for fast compression) shows one refutable candidate out of four examined, suggesting some prior work overlap in this limited search scope. The second contribution (spatio-temporal cross-modal context structure) examined six candidates with none clearly refuting it, indicating potentially stronger novelty within the examined set. The third contribution (joint geometry-intensity backbone) also found no refutations among four candidates. These statistics reflect a focused search rather than exhaustive coverage, so unexamined literature may contain additional relevant prior work.

Based on the limited search of fourteen candidates, the work appears to occupy a moderately novel position, particularly in its integration of cross-modal camera context and joint geometry-intensity compression within a real-time framework. The sparse population of its taxonomy leaf and the mixed refutation results suggest incremental advancement over existing real-time neural methods, though the restricted search scope prevents definitive conclusions about broader field-level novelty.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
14
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: LiDAR point cloud compression. The field addresses the challenge of efficiently encoding massive three-dimensional point clouds captured by LiDAR sensors, balancing storage and transmission costs against reconstruction quality. The taxonomy reveals a diverse landscape organized around six main branches: Compression Architecture and Methodology explores foundational encoding strategies ranging from classical octree-based schemes like LASzip[3] to modern deep learning approaches; Data Representation and Encoding examines how point clouds are transformed into compressible formats, including range images and voxel grids; Temporal and Spatial Redundancy Exploitation focuses on leveraging correlations across frames and within scenes; Application-Driven and Context-Aware Compression tailors methods to specific domains such as autonomous driving or geospatial mapping; Surveys and Comparative Studies provide overviews like LiDAR Compression Survey[2] and Point Cloud Survey[1]; and Specialized Techniques and Auxiliary Methods address niche challenges like rate control and hardware acceleration. Within the deep learning branch, a particularly active line of work pursues real-time and lightweight neural compression, balancing model complexity against latency constraints critical for automotive and edge deployment scenarios. Neural LiDAR Compression[0] sits squarely in this cluster, emphasizing efficient neural architectures that can operate under strict computational budgets. Nearby works such as Reno[6] and FLiCR[36] similarly target real-time performance, though they may differ in their choice of network backbone or entropy coding strategy. Meanwhile, other branches explore hybrid approaches that combine classical geometry coding with learned components, as seen in Deep Hybrid Compression[29], or focus on application-specific optimizations like Automotive LiDAR Survey[5]. The central tension across these directions involves trading off compression ratio, reconstruction fidelity, and computational overhead, with ongoing questions about how best to exploit temporal redundancy and adapt to varying point densities in dynamic scenes.

Claimed Contributions

RangeCM: 2D context model for fast neural LiDAR compression

The authors introduce RangeCM, a neural compression method that uses 2D context models operating on range images instead of computationally expensive 3D contexts. This approach achieves faster coding speed while maintaining high compression efficiency and enables joint geometry-intensity compression.

4 retrieved papers
Can Refute
Comprehensive spatio-temporal cross-modal context structure

The authors propose a multi-faceted context modeling approach that combines multi-scale spatial contexts for intra-frame prediction, optical-flow-based temporal contexts for inter-frame prediction, and cross-modal camera contexts using deformable attention to improve compression performance in the 2D domain.

6 retrieved papers
Joint geometry-intensity compression backbone

The authors design a unified neural network backbone that compresses both geometry and intensity attributes simultaneously using a single hybrid context model, reducing redundant computation compared to existing methods that use separate networks for each modality.

4 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

RangeCM: 2D context model for fast neural LiDAR compression

The authors introduce RangeCM, a neural compression method that uses 2D context models operating on range images instead of computationally expensive 3D contexts. This approach achieves faster coding speed while maintaining high compression efficiency and enables joint geometry-intensity compression.

Contribution

Comprehensive spatio-temporal cross-modal context structure

The authors propose a multi-faceted context modeling approach that combines multi-scale spatial contexts for intra-frame prediction, optical-flow-based temporal contexts for inter-frame prediction, and cross-modal camera contexts using deformable attention to improve compression performance in the 2D domain.

Contribution

Joint geometry-intensity compression backbone

The authors design a unified neural network backbone that compresses both geometry and intensity attributes simultaneously using a single hybrid context model, reducing redundant computation compared to existing methods that use separate networks for each modality.

Table of Contents