# Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGB

arXiv: https://arxiv.org/abs/2411.19474 · HTML: https://mlsanity.com/paper/2411.19474/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 29 of 29)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data](https://mlsanity.com/paper/2111.08897/) (2021)
- [High-quality Surface Reconstruction using Gaussian Surfels](https://mlsanity.com/paper/2404.17774/) (2024)
- [T"oRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis](https://mlsanity.com/paper/2109.15271/) (2021)
- [NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields](https://mlsanity.com/paper/2304.14811/) (2023)
- [Non-line-of-sight Imaging](https://mlsanity.com/paper/2005.08026/) (2020)
- [DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image](https://mlsanity.com/paper/2209.13362/) (2022)
- [Dynamic LiDAR Re-simulation using Compositional Neural Fields](https://mlsanity.com/paper/2312.05247/) (2023)
- [Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction](https://mlsanity.com/paper/2307.09555/) (2023)
- [Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar](https://mlsanity.com/paper/2405.04662/) (2024)
- [Consistent Direct Time-of-Flight Video Depth Super-Resolution](https://mlsanity.com/paper/2211.08658/) (2022)
- [Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging](https://mlsanity.com/paper/2205.01679/) (2022)
- [Towards 3D Vision with Low-Cost Single-Photon Cameras](https://mlsanity.com/paper/2403.17801/) (2024)
- [Multi-sensor large-scale dataset for multi-view 3D reconstruction](https://mlsanity.com/paper/2203.06111/) (2022)
- [NLOS-NeuS: Non-line-of-sight Neural Implicit Surface](https://mlsanity.com/paper/2303.12280/) (2023)
- [AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion](https://mlsanity.com/paper/2402.03309/) (2024)
- [PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar](https://mlsanity.com/paper/2312.14239/) (2023)
- [Flying with Photons: Rendering Novel Views of Propagating Light](https://mlsanity.com/paper/2404.06493/) (2024)
- [3D Scene Inference from Transient Histograms](https://mlsanity.com/paper/2211.05094/) (2022)
- [Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar](https://mlsanity.com/paper/2408.12191/) (2024)
- [FloatingFusion: Depth from ToF and Image-stabilized Stereo Cameras](https://mlsanity.com/paper/2210.02785/) (2022)
- [Role of Transients in Two-Bounce Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2304.01308/) (2023)
- [Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms](https://mlsanity.com/paper/2308.13473/) (2023)
- [Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditions](https://mlsanity.com/paper/2411.06181/) (2024)

## Built on by (most cited first, 10 of 10)

- [A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation](https://mlsanity.com/paper/2511.19004/) (2025)
- [Recovering Parametric Scenes from Very Few Time-of-Flight Pixels](https://mlsanity.com/paper/2509.16132/) (2025)
- [DensifyBeforehand: LiDAR-assisted Content-aware Densification for Efficient and Quality 3D Gaussian Splatting](https://mlsanity.com/paper/2511.19294/) (2025)
- [Task-Driven Implicit Representations for Automated Design of LiDAR Systems](https://mlsanity.com/paper/2505.22344/) (2025)
- [SuperEx: Enhancing Indoor Mapping and Exploration using Non-Line-of-Sight Perception](https://mlsanity.com/paper/2510.10506/) (2025)
- [Spatial Calibration of Diffuse LiDARs](https://mlsanity.com/paper/2603.06531/) (2026)
- [DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs](https://mlsanity.com/paper/2604.16201/) (2026)
- [Imaging Hidden Objects with Consumer LiDAR via Motion Induced Sampling](https://mlsanity.com/paper/2605.17865/) (2026)
- [Non-line-of-sight imaging with arbitrary relay surface geometries via 3D Gaussian Transient Rendering](https://mlsanity.com/paper/2606.21270/) (2026)
- [T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation](https://mlsanity.com/paper/2606.30147/) (2026)

## Abstract (arXiv)

3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from time-of-flight measurements of a coarse grid of projected dots. Yet, these sparse LiDARs struggle with scene coverage on limited input views, leaving large gaps in depth information. In this work, we propose using an alternative class of "blurred" LiDAR that emits a diffuse flash, greatly improving scene coverage but introducing spatial ambiguity from mixed time-of-flight measurements across a wide field of view. To handle these ambiguities, we propose leveraging the complementary strengths of diffuse LiDAR with RGB. We introduce a Gaussian surfel-based rendering framework with a scene-adaptive loss function that dynamically balances RGB and diffuse LiDAR signals. We demonstrate that, surprisingly, diffuse LiDAR can outperform traditional sparse LiDAR, enabling robust 3D scanning with accurate color and geometry estimation in challenging environments.