# Self-Annotated 3D Geometric Learning for Smeared Points Removal

arXiv: https://arxiv.org/abs/2311.09029 · HTML: https://mlsanity.com/paper/2311.09029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://mlsanity.com/paper/1908.04616/) (2019)
- [Understanding Self-Training for Gradual Domain Adaptation](https://mlsanity.com/paper/2002.11361/) (2020)
- [Cycle Self-Training for Domain Adaptation](https://mlsanity.com/paper/2103.03571/) (2021)
- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning](https://mlsanity.com/paper/1904.07615/) (2019)
- [A Dataset for Developing and Benchmarking Active Vision](https://mlsanity.com/paper/1702.08272/) (2017)
- [Multiview RGB-D Dataset for Object Instance Detection](https://mlsanity.com/paper/1609.07826/) (2016)
- [Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset](https://mlsanity.com/paper/1807.10376/) (2018)
- [Self-Supervised Deep Depth Denoising](https://mlsanity.com/paper/1909.01193/) (2019)
- [DeepToF: Off-the-Shelf Real-Time Correction of Multipath Interference in Time-of-Flight Imaging](https://mlsanity.com/paper/1805.09305/) (2018)
- [Resolving Multi-path Interference in Time-of-Flight Imaging via Modulation Frequency Diversity and Sparse Regularization](https://mlsanity.com/paper/1404.1116/) (2014)
- [RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising](https://mlsanity.com/paper/2111.15513/) (2021)

## Built on by (most cited first, 3 of 3)

- [DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interaction](https://mlsanity.com/paper/2503.05484/) (2025)
- [NOVA-3D: Non-overlapped Views for 3D Anime Character Reconstruction](https://mlsanity.com/paper/2405.12505/) (2024)
- [RemoCap: Disentangled Representation Learning for Motion Capture](https://mlsanity.com/paper/2405.12724/) (2024)

## Abstract (arXiv)

There has been significant progress in improving the accuracy and quality of consumer-level dense depth sensors. Nevertheless, there remains a common depth pixel artifact which we call smeared points. These are points not on any 3D surface and typically occur as interpolations between foreground and background objects. As they cause fictitious surfaces, these points have the potential to harm applications dependent on the depth maps. Statistical outlier removal methods fare poorly in removing these points as they tend also to remove actual surface points. Trained network-based point removal faces difficulty in obtaining sufficient annotated data. To address this, we propose a fully self-annotated method to train a smeared point removal classifier. Our approach relies on gathering 3D geometric evidence from multiple perspectives to automatically detect and annotate smeared points and valid points. To validate the effectiveness of our method, we present a new benchmark dataset: the Real Azure-Kinect dataset. Experimental results and ablation studies show that our method outperforms traditional filters and other self-annotated methods. Our work is publicly available at https://github.com/wangmiaowei/wacv2024_smearedremover.git.