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Jianning Deng

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3 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis

Jianning Deng
Kartic Subr
Hakan Bilen

We propose a novel unsupervised method to learn pose and part-segmentation of articulated objects with rigid parts. Given two observations of an object in different articulation states, our method learns the geometry and appearance of object parts by using an implicit model from the first observation, distills the part segmentation and articulation from the second observation while rendering the latter observation. Additionally, to tackle the complexities in the joint optimization of part segmentation and articulation, we propose a voxel grid based initialization strategy and a decoupled optimization procedure. Compared to the prior unsupervised work, our model obtains significantly better performance, generalizes to objects with multiple parts while it can be efficiently from few views for the latter observation.

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ICRA Conference 2024 Conference Paper

Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation

Jianning Deng
Gabriel Chan
Hantao Zhong
Chris Xiaoxuan Lu

This paper presents a novel framework for robust 3D object detection from point clouds via cross-modal hallucination. Our proposed approach is agnostic to either hallucination direction between LiDAR and 4D radar. We introduce multiple alignments on both spatial and feature levels to achieve simultaneous backbone refinement and hallucination generation. Specifically, spatial alignment is proposed to deal with the geometry discrepancy for better instance matching between LiDAR and radar. The feature alignment step further bridges the intrinsic attribute gap between the sensing modalities and stabilizes the training. The trained object detection models can deal with difficult detection cases better, even though only single-modal data is used as the input during the inference stage. Extensive experiments on the View-of-Delft (VoD) dataset show that our proposed method outperforms the state-of-the-art (SOTA) methods for both radar and LiDAR object detection while maintaining competitive efficiency in runtime.

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IROS Conference 2023 Conference Paper

Feature-based Visual Odometry for Bronchoscopy: A Dataset and Benchmark

Jianning Deng
Peize Li
Kevin Dhaliwal
Chris Xiaoxuan Lu
Mohsen Khadem

Bronchoscopy is a medical procedure that involves the insertion of a flexible tube with a camera into the airways to survey, diagnose and treat lung diseases. Due to the complex branching anatomical structure of the bronchial tree and the similarity of the inner surfaces of the segmental airways, navigation systems are now being routinely used to guide the operator during procedures to access the lung periphery. Current navigation systems rely on sensor-integrated bronchoscopes to track the position of the bronchoscope in real-time. This approach has limitations, including increased cost and limited use in non-specialized settings. To address this issue, researchers have proposed visual odometry algorithms to track the bronchoscope camera without the need for external sensors. However, due to the lack of publicly available datasets, limited progress is made. To this end, we have developed a database of bronchoscopy videos in a phantom lung model and ex-vivo human lungs. The dataset contains 34 video sequences with over 23, 000 frames with odometry ground truth data collected using electromagnetic tracking sensors. With our dataset, we empower the robotics and machine learning community to advance the field. We share our insights on challenges in endoscopic visual odometry. Furthermore, we provide benchmark results for this dataset. State-of-the-art feature extraction algorithms including SIFT, ORB, Superpoint, Shi- Tomasi, and LoFTR are tested on this dataset. The benchmark results demonstrate that the LoFTR algorithm outperforms other approaches, but still has significant errors in the presence of rapid movements and occlusions.

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