# MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation

arXiv: https://arxiv.org/abs/2110.00464 · HTML: https://mlsanity.com/paper/2110.00464/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 76 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 30 of 58)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://mlsanity.com/paper/1907.06038/) (2019)
- [Disentangling Monocular 3D Object Detection](https://mlsanity.com/paper/1905.12365/) (2019)
- [SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation](https://mlsanity.com/paper/2002.10111/) (2020)
- [Orthographic Feature Transform for Monocular 3D Object Detection](https://mlsanity.com/paper/1811.08188/) (2018)
- [DPOD: 6D Pose Object Detector and Refiner](https://mlsanity.com/paper/1902.11020/) (2019)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1906.06310/) (2019)
- [Objects are Different: Flexible Monocular 3D Object Detection](https://mlsanity.com/paper/2104.02323/) (2021)
- [Learning Depth-Guided Convolutions for Monocular 3D Object Detection](https://mlsanity.com/paper/1912.04799/) (2019)
- [MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships](https://mlsanity.com/paper/2003.00504/) (2020)
- [Train in Germany, Test in The USA: Making 3D Object Detectors Generalize](https://mlsanity.com/paper/2005.08139/) (2020)
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://mlsanity.com/paper/2001.03343/) (2020)
- [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019)
- [Delving into Localization Errors for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.16237/) (2021)
- [Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://mlsanity.com/paper/1903.11444/) (2019)
- [ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape](https://mlsanity.com/paper/1812.02781/) (2018)
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://mlsanity.com/paper/1708.01566/) (2017)
- [Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud](https://mlsanity.com/paper/1903.09847/) (2019)
- [Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction](https://mlsanity.com/paper/1904.01690/) (2019)

## Built on by (most cited first, 16 of 16)

- [Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction](https://mlsanity.com/paper/2304.06714/) (2023)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM](https://mlsanity.com/paper/2210.16204/) (2022)
- [OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects](https://mlsanity.com/paper/2407.08711/) (2024)
- [NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization](https://mlsanity.com/paper/2305.17763/) (2023)
- [Segment, Lift and Fit: Automatic 3D Shape Labeling from 2D Prompts](https://mlsanity.com/paper/2407.11382/) (2024)
- [Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation](https://mlsanity.com/paper/2403.19319/) (2024)
- [Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation](https://mlsanity.com/paper/2312.04530/) (2023)
- [What You See Is What You Detect: Towards better Object Densification in 3D detection](https://mlsanity.com/paper/2310.17842/) (2023)
- [SDVRF: Sparse-to-Dense Voxel Region Fusion for Multi-modal 3D Object Detection](https://mlsanity.com/paper/2304.08304/) (2023)
- [Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection](https://mlsanity.com/paper/2306.10921/) (2023)
- [SurfaceAug: Closing the Gap in Multimodal Ground Truth Sampling](https://mlsanity.com/paper/2312.03808/) (2023)
- [MonoLite3D: Lightweight 3D Object Properties Estimation](https://mlsanity.com/paper/2503.02201/) (2025)
- [S-LAM3D: Segmentation-Guided Monocular 3D Object Detection via Feature Space Fusion](https://mlsanity.com/paper/2509.05999/) (2025)
- [Autonomous Driving Research Requires a Community-Driven Data Paradigm](https://mlsanity.com/paper/2610.08825/) (2026)

## Abstract (arXiv)

Monocular 3D object detection has recently shown promising results, however there remain challenging problems. One of those is the lack of invariance to different camera intrinsic parameters, which can be observed across different 3D object datasets. Little effort has been made to exploit the combination of heterogeneous 3D object datasets. In contrast to general intuition, we show that more data does not automatically guarantee a better performance, but rather, methods need to have a degree of 'camera independence' in order to benefit from large and heterogeneous training data. In this paper we propose a category-level pose estimation method based on instance segmentation, using camera independent geometric reasoning to cope with the varying camera viewpoints and intrinsics of different datasets. Every pixel of an instance predicts the object dimensions, the 3D object reference points projected in 2D image space and, optionally, the local viewing angle. Camera intrinsics are only used outside of the learned network to lift the predicted 2D reference points to 3D. We surpass camera independent methods on the challenging KITTI3D benchmark and show the key benefits compared to camera dependent methods.