# LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion

arXiv: https://arxiv.org/abs/2408.03238 · HTML: https://mlsanity.com/paper/2408.03238/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 30 of 36)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [Semantic Amodal Segmentation](https://mlsanity.com/paper/1509.01329/) (2015)
- [Unseen Object Instance Segmentation for Robotic Environments](https://mlsanity.com/paper/2007.08073/) (2020)
- [6-DOF Grasping for Target-driven Object Manipulation in Clutter](https://mlsanity.com/paper/1912.03628/) (2019)
- [Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation](https://mlsanity.com/paper/2007.15157/) (2020)
- [SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2106.14193/) (2021)
- [Towards Segmenting Anything That Moves](https://mlsanity.com/paper/1902.03715/) (2019)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [Amodal Segmentation Based on Visible Region Segmentation and Shape Prior](https://mlsanity.com/paper/2012.05598/) (2020)
- [Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling](https://mlsanity.com/paper/2109.11103/) (2021)
- [The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation](https://mlsanity.com/paper/1907.13236/) (2019)
- [AISFormer: Amodal Instance Segmentation with Transformer](https://mlsanity.com/paper/2210.06323/) (2022)
- [A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter](https://mlsanity.com/paper/2302.12610/) (2023)
- [Occlusion-Aware Video Object Inpainting](https://mlsanity.com/paper/2108.06765/) (2021)
- [Coarse-to-Fine Amodal Segmentation with Shape Prior](https://mlsanity.com/paper/2308.16825/) (2023)
- [OpenVIS: Open-vocabulary Video Instance Segmentation](https://mlsanity.com/paper/2305.16835/) (2023)
- [End-to-End Learning of Semantic Grasping](https://mlsanity.com/paper/1707.01932/) (2017)
- [A Deep Learning Approach to Grasping the Invisible](https://mlsanity.com/paper/1909.04840/) (2019)
- [Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions](https://mlsanity.com/paper/2205.04028/) (2022)
- [Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation](https://mlsanity.com/paper/1905.12898/) (2019)
- [Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter](https://mlsanity.com/paper/2008.06073/) (2020)
- [Self-supervised Amodal Video Object Segmentation](https://mlsanity.com/paper/2210.12733/) (2022)
- [Efficient learning of goal-oriented push-grasping synergy in clutter](https://mlsanity.com/paper/2103.05405/) (2021)

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

- [CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image](https://mlsanity.com/paper/2504.11230/) (2025)
- [Improving Robotic Manipulation Robustness via NICE Scene Surgery](https://mlsanity.com/paper/2511.22777/) (2025)

## Abstract (arXiv)

This paper addresses the challenge of perceiving complete object shapes through visual perception. While prior studies have demonstrated encouraging outcomes in segmenting the visible parts of objects within a scene, amodal segmentation, in particular, has the potential to allow robots to infer the occluded parts of objects. To this end, this paper introduces a new framework that explores amodal segmentation for robotic grasping in cluttered scenes, thus greatly enhancing robotic grasping abilities. Initially, we use a conventional segmentation algorithm to detect the visible segments of the target object, which provides shape priors for completing the full object mask. Particularly, to explore how to utilize semantic features from RGB images and geometric information from depth images, we propose a Linear-fusion Attention-guided Convolutional Network (LAC-Net). LAC-Net utilizes the linear-fusion strategy to effectively fuse this cross-modal data, and then uses the prior visible mask as attention map to guide the network to focus on target feature locations for further complete mask recovery. Using the amodal mask of the target object provides advantages in selecting more accurate and robust grasp points compared to relying solely on the visible segments. The results on different datasets show that our method achieves state-of-the-art performance. Furthermore, the robot experiments validate the feasibility and robustness of this method in the real world. Our code and demonstrations are available on the project page: https://jrryzh.github.io/LAC-Net.