# Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection

arXiv: https://arxiv.org/abs/2006.04356 · HTML: https://mlsanity.com/paper/2006.04356/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 30 of 31)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://mlsanity.com/paper/2203.11139/) (2022)
- [SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud](https://mlsanity.com/paper/2104.09804/) (2021)
- [SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation](https://mlsanity.com/paper/2108.06709/) (2021)
- [Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.16470/) (2021)
- [LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector](https://mlsanity.com/paper/2108.08258/) (2021)
- [Behind the Curtain: Learning Occluded Shapes for 3D Object Detection](https://mlsanity.com/paper/2112.02205/) (2021)
- [Progressive Coordinate Transforms for Monocular 3D Object Detection](https://mlsanity.com/paper/2108.05793/) (2021)
- [VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion](https://mlsanity.com/paper/2111.14382/) (2021)
- [HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection](https://mlsanity.com/paper/2104.00902/) (2021)
- [A Voxel Graph CNN for Object Classification with Event Cameras](https://mlsanity.com/paper/2106.00216/) (2021)
- [The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection](https://mlsanity.com/paper/2112.14023/) (2021)
- [Spatial Pruned Sparse Convolution for Efficient 3D Object Detection](https://mlsanity.com/paper/2209.14201/) (2022)
- [DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection](https://mlsanity.com/paper/2207.10909/) (2022)
- [From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder](https://mlsanity.com/paper/2108.03648/) (2021)
- [3D Object Detection with a Self-supervised Lidar Scene Flow Backbone](https://mlsanity.com/paper/2205.00705/) (2022)
- [Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection](https://mlsanity.com/paper/2211.13067/) (2022)
- [Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud](https://mlsanity.com/paper/2108.03634/) (2021)
- [SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud](https://mlsanity.com/paper/2103.15396/) (2021)
- [AGO-Net: Association-Guided 3D Point Cloud Object Detection Network](https://mlsanity.com/paper/2208.11658/) (2022)
- [StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection](https://mlsanity.com/paper/2301.01615/) (2023)
- [ImpDet: Exploring Implicit Fields for 3D Object Detection](https://mlsanity.com/paper/2203.17240/) (2022)
- [CVFNet: Real-time 3D Object Detection by Learning Cross View Features](https://mlsanity.com/paper/2203.06585/) (2022)
- [BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap](https://mlsanity.com/paper/2303.02000/) (2023)
- [NIV-SSD: Neighbor IoU-Voting Single-Stage Object Detector From Point Cloud](https://mlsanity.com/paper/2401.12447/) (2024)
- [Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection](https://mlsanity.com/paper/2403.01978/) (2024)
- [3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning](https://mlsanity.com/paper/2208.14796/) (2022)
- [SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanism](https://mlsanity.com/paper/2407.01239/) (2024)
- [PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point Dilation](https://mlsanity.com/paper/2502.07822/) (2025)
- [Intrinsic-feature-guided 3D Object Detection](https://mlsanity.com/paper/2504.00382/) (2025)

## Abstract (arXiv)

Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques. Owing to the severe spatial occlusion and inherent variance of point density with the distance to sensors, appearance of a same object varies a lot in point cloud data. Designing robust feature representation against such appearance changes is hence the key issue in a 3D object detection method. In this paper, we innovatively propose a domain adaptation like approach to enhance the robustness of the feature representation. More specifically, we bridge the gap between the perceptual domain where the feature comes from a real scene and the conceptual domain where the feature is extracted from an augmented scene consisting of non-occlusion point cloud rich of detailed information. This domain adaptation approach mimics the functionality of the human brain when proceeding object perception. Extensive experiments demonstrate that our simple yet effective approach fundamentally boosts the performance of 3D point cloud object detection and achieves the state-of-the-art results.