# Prior based Sampling for Adaptive LiDAR

arXiv: https://arxiv.org/abs/2304.07099 · HTML: https://mlsanity.com/paper/2304.07099/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Sparsity Invariant CNNs](https://mlsanity.com/paper/1708.06500/) (2017)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Non-Local Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2007.10042/) (2020)
- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [Decomposing Motion and Content for Natural Video Sequence Prediction](https://mlsanity.com/paper/1706.08033/) (2017)
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://mlsanity.com/paper/1709.07492/) (2017)
- [Deep Spatial Autoencoders for Visuomotor Learning](https://mlsanity.com/paper/1509.06113/) (2015)
- [SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation](https://mlsanity.com/paper/2206.08367/) (2022)
- [Learning Depth with Convolutional Spatial Propagation Network](https://mlsanity.com/paper/1810.02695/) (2018)
- [DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image](https://mlsanity.com/paper/1812.00488/) (2018)
- [Learning to Decompose and Disentangle Representations for Video Prediction](https://mlsanity.com/paper/1806.04166/) (2018)
- [In Defense of Classical Image Processing: Fast Depth Completion on the CPU](https://mlsanity.com/paper/1802.00036/) (2018)
- [Learning to Generate Long-term Future via Hierarchical Prediction](https://mlsanity.com/paper/1704.05831/) (2017)
- [Sparse and noisy LiDAR completion with RGB guidance and uncertainty](https://mlsanity.com/paper/1902.05356/) (2019)
- [SampleNet: Differentiable Point Cloud Sampling](https://mlsanity.com/paper/1912.03663/) (2019)
- [Dual Motion GAN for Future-Flow Embedded Video Prediction](https://mlsanity.com/paper/1708.00284/) (2017)
- [SDCNet: Video Prediction Using Spatially-Displaced Convolution](https://mlsanity.com/paper/1811.00684/) (2018)
- [Propagating Confidences through CNNs for Sparse Data Regression](https://mlsanity.com/paper/1805.11913/) (2018)
- [Deep Convolutional Compressed Sensing for LiDAR Depth Completion](https://mlsanity.com/paper/1803.08949/) (2018)
- [Learning to Linearize Under Uncertainty](https://mlsanity.com/paper/1506.03011/) (2015)
- [DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles](https://mlsanity.com/paper/2007.02438/) (2020)
- [Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction](https://mlsanity.com/paper/1812.10836/) (2018)
- [Adaptive LiDAR Sampling and Depth Completion using Ensemble Variance](https://mlsanity.com/paper/2007.13834/) (2020)
- [SGTBN: Generating Dense Depth Maps from Single-Line LiDAR](https://mlsanity.com/paper/2106.12994/) (2021)
- [How to Guide Adaptive Depth Sampling?](https://mlsanity.com/paper/2205.10202/) (2022)

## Built on by (most cited first, 2 of 2)

- [Progressive Depth Decoupling and Modulating for Flexible Depth Completion](https://mlsanity.com/paper/2405.09342/) (2024)
- [CARE: Camera-Residual Reserves for First Sightings in Adaptive LiDAR Sensing](https://mlsanity.com/paper/2608.24282/) (2026)

## Abstract (arXiv)

We propose SampleDepth, a Convolutional Neural Network (CNN), that is suited for an adaptive LiDAR. Typically,LiDAR sampling strategy is pre-defined, constant and independent of the observed scene. Instead of letting a LiDAR sample the scene in this agnostic fashion, SampleDepth determines, adaptively, where it is best to sample the current frame. To do that, SampleDepth uses depth samples from previous time steps to predict a sampling mask for the current frame. Crucially, SampleDepth is trained to optimize the performance of a depth completion downstream task. SampleDepth is evaluated on two different depth completion networks and two LiDAR datasets, KITTI Depth Completion and the newly introduced synthetic dataset, SHIFT. We show that SampleDepth is effective and suitable for different depth completion downstream tasks.