# Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

arXiv: https://arxiv.org/abs/2406.04603 · HTML: https://mlsanity.com/paper/2406.04603/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [Dense Regression Network for Video Grounding](https://mlsanity.com/paper/2004.03545/) (2020)
- [MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment](https://mlsanity.com/paper/1812.00087/) (2018)
- [Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video](https://mlsanity.com/paper/2001.06680/) (2020)
- [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://mlsanity.com/paper/2303.04995/) (2023)
- [ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data](https://mlsanity.com/paper/2210.16467/) (2022)
- [Two-Stream Regression Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2305.10044/) (2023)
- [TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2306.14406/) (2023)
- [TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2308.05355/) (2023)

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

- [SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis](https://mlsanity.com/paper/2406.13963/) (2024)
- [RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning](https://mlsanity.com/paper/2601.14703/) (2026)
- [Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction](https://mlsanity.com/paper/2406.12186/) (2024)

## Abstract (arXiv)

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods.