LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields

• URL: http://arxiv.org/abs/2605.15496v1
• Date: Fri, 15 May 2026 00:22:31 GMT
• Title: LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields
• Abstract summary: LAPS is a replay management framework for incremental neural mapping.<n>It improves replay retention and replay allocation during online updates.<n>It consistently improves reconstruction completeness while maintaining competitive geometric accuracy.
• Score: 11.70788501966735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural distance fields offer a compact and continuous representation of 3D geometry, making them attractive for incremental LiDAR mapping. However, their online optimization is vulnerable to catastrophic forgetting, where new observations can degrade previously reconstructed geometry. Replay-based training is commonly used to address this issue, but existing methods typically rely on passive replay buffers and uniform sampling, which can waste memory on redundant observations and under-train poorly constrained regions. We propose LAPS, a replay management framework for incremental neural mapping that improves both replay retention and replay allocation during online updates. LAPS combines reliability-based active pooling to retain reliable historical samples under limited memory with uncertainty-guided active sampling to focus optimization on under-constrained regions. Experiments on synthetic and real-world benchmarks show that LAPS consistently improves reconstruction completeness while maintaining competitive geometric accuracy. On Oxford Spires, it improves recall by 4.66 pp and F1-score by 3.79 pp over PIN-SLAM on the Blenheim Palace 05 sequence. We release our open source implementation at: https://github.com/dongjae0107/LAPS.

Related papers

• Geometry-Aware Online Mapping for 3D Gaussian Splatting SLAM [1.988537471957171]
  Recent 3D Gaussian Splatting (3DGS) has enabled efficient view computation and is rapidly being adopted in simultaneous localization and mapping (SLAM) systems for online mapping.<n>In this work, we propose three geometry-aware methods that operate in a decoupled 3DGS-SLAM setting.<n>Our results show consistent improvements in rendering quality with negligible overhead, highlighting the coupling between photometric residuals and pose uncertainty in online SLAM.
  arXiv  Detail & Related papers  (2026-08-14T21:20:16Z)
• ReCal3R: Reliability-Calibrated Learning Rates for Streaming 3D Reconstruction [57.10941470313318]
  Streaming 3D reconstruction relies on a compact recurrent scene state to process long image streams in linear time and bounded memory.<n>We introduce ReCal3R, a reliability-calibrated learning rate method for recurrent 3D reconstruction.
  arXiv  Detail & Related papers  (2026-07-06T17:35:18Z)
• Replay What Matters: Off-Policy Replay for Efficient LLM Reinforcement Unlearning [19.683904518981624]
  ReRULE is an off-policy replay enhancement for reinforcement unlearning.<n>We show that ReRULE yields a tighter hard-case convergence bound than pure on-policy RULE.
  arXiv  Detail & Related papers  (2026-06-13T14:52:40Z)
• Prompt replay: speeding up grpo with on-policy reuse of high-signal prompts [0.0]
  Reinforcement learning with verifiable rewards (RLVR) plays a crucial role in expanding the capacities of reasoning, but GRPO-style training is dominated by expensive rollouts and wastes compute on unusable prompts.<n>We propose Prompt Replay, an overhead-free online data selection method for GRPO that reuses prompts only (not trajectories) to preserve on-policy optimization.
  arXiv  Detail & Related papers  (2026-03-22T11:39:51Z)
• HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning [78.12979615107564]
  HeaPA consistently improves accuracy and reaches target performance with fewer computations.<n>Our analyses suggest these gains come from frontier-focused sampling and on-policy pool growth.
  arXiv  Detail & Related papers  (2026-01-30T01:31:17Z)
• FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning [63.20028888397869]
  FOREVER (FORgEtting curVe-inspired mEmory) is a novel framework that aligns replay schedules with a model-centric notion of time.<n>Building on this approach, FOREVER incorporates a forgetting curve-based replay scheduler to determine when to replay and an intensity-aware regularization mechanism to adaptively control how to replay.
  arXiv  Detail & Related papers  (2026-01-07T13:55:14Z)
• Pseudo Depth Meets Gaussian: A Feed-forward RGB SLAM Baseline [64.42938561167402]
  We propose an online 3D reconstruction method using 3D Gaussian-based SLAM, combined with a feed-forward recurrent prediction module.<n>This approach replaces slow test-time optimization with fast network inference, significantly improving tracking speed.<n>Our method achieves performance on par with the state-of-the-art SplaTAM, while reducing tracking time by more than 90%.
  arXiv  Detail & Related papers  (2025-08-06T16:16:58Z)
• GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning [20.112448377660854]
  We introduce Grid-based Patch Sampling (GPS), a lightweight strategy for distilling informative memory samples without relying on a trainable model.<n>GPS generates informative samples by sampling a subset of pixels from the original image, yielding compact low-resolution representations.<n>GPS can be seamlessly integrated into existing replay frameworks, leading to 3%-4% improvements in average end accuracy under memory-constrained settings.
  arXiv  Detail & Related papers  (2025-04-14T16:58:02Z)
• Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training [71.16258800411696]
  Reinforcement learning (RL) is a critical component of large language model (LLM) post-training.<n>Existing on-policy algorithms used for post-training are inherently incompatible with the use of experience replay buffers.<n>We propose efficiently obtaining this benefit of replay buffers via Trajectory Balance with Asynchrony (TBA)
  arXiv  Detail & Related papers  (2025-03-24T17:51:39Z)
• Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning [22.00843101957619]
  In online continual learning, a neural network incrementally learns from a non-i.i.d. data stream. Our work demonstrates a limitation of this approach: neural networks trained with experience replay tend to have unstable optimization trajectories. We present Layerwise Proximal Replay (LPR), which balances learning from new and replay data while only allowing for gradual changes in the hidden activation of past data.
  arXiv  Detail & Related papers  (2024-02-14T19:34:28Z)
• Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks [60.88792564390274]
  Neighborhood Mixup Experience Replay (NMER) is a geometrically-grounded replay buffer that interpolates transitions with their closest neighbors in state-action space. We observe that NMER improves sample efficiency by an average 94% (TD3) and 29% (SAC) over baseline replay buffers.
  arXiv  Detail & Related papers  (2022-05-18T02:44:08Z)
• Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild [104.61677518999976]
  We propose Pixel-in-Pixel Net (PIPNet) for facial landmark detection. The proposed model is equipped with a novel detection head based on heatmap regression. To further improve the cross-domain generalization capability of PIPNet, we propose self-training with curriculum.
  arXiv  Detail & Related papers  (2020-03-08T12:23:42Z)

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.