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Mitigating Perspective Distortion-induced Shape Ambiguity in Image Crops

Authors

Aditya Prakash, Arjun Gupta, Saurabh Gupta
Type: Preprint
Publication Date: 2023-01-01
Citations: 0
DOI: https://doi.org/10.48550/arxiv.2312.06594

Abstract

Objects undergo varying amounts of perspective distortion as they move across a camera's field of view. Models for predicting 3D from a single image often work with crops around the object of interest and ignore the location of the object in the camera's field of view. We note that ignoring this location information further exaggerates the inherent ambiguity in making 3D inferences from 2D images and can prevent models from even fitting to the training data. To mitigate this ambiguity, we propose Intrinsics-Aware Positional Encoding (KPE), which incorporates information about the location of crops in the image and camera intrinsics. Experiments on three popular 3D-from-a-single-image benchmarks: depth prediction on NYU, 3D object detection on KITTI & nuScenes, and predicting 3D shapes of articulated objects on ARCTIC, show the benefits of KPE.

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OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

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Self-Supervised Pre-Training with Masked Shape Prediction for 3D Scene Understanding

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GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D

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We introduce a pioneering approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE). … We introduce a pioneering approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE). Unlike the conventional method of random masking, our technique utilizes a teacher-student model to focus on intricate areas within the data, guiding the model's focus toward regions with higher geometric complexity. This strategy is grounded in the hypothesis that concentrating on harder patches yields a more robust feature representation, as evidenced by the improved performance on downstream tasks. Our method also presents a complete-to-partial feature-level knowledge distillation technique designed to guide the prediction of geometric complexity utilizing a comprehensive context from feature-level information. Extensive experiments confirm our method's superiority over State-Of-The-Art (SOTA) baselines, demonstrating marked improvements in classification, and few-shot tasks.
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Self-supervised Learning of Point Clouds via Orientation Estimation

2020-08-01
Omid Poursaeed , Tianxing Jiang , Han Qiao +2 more
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Self-supervised Learning of Point Clouds via Orientation Estimation

2020-01-01
Omid Poursaeed , Tianxing Jiang , Han Qiao +2 more
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View PDF Chat

MOT-DETR: 3D single shot detection and tracking with transformers to build 3D representations for agro-food robots

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In the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due … In the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the objects detected by the robot over multiple viewpoints. Most multi-object tracking (MOT) algorithms are designed for high frame rate sequences and struggle with the occlusions generated by robots' motions and 3D environments. In this paper, we introduce MOT-DETR, a novel approach to detect and track objects in 3D over time using a combination of convolutional networks and transformers. Our method processes 2D and 3D data, and employs a transformer architecture to perform data fusion. We show that MOT-DETR outperforms state-of-the-art multi-object tracking methods. Furthermore, we prove that MOT-DETR can leverage 3D data to deal with long-term occlusions and large frame-to-frame distances better than state-of-the-art methods. Finally, we show how our method is resilient to camera pose noise that can affect the accuracy of point clouds. The implementation of MOT-DETR can be found here: https://github.com/drapado/mot-detr.
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2023-10-01
Jihao Liu , Tai Wang , Boxiao Liu +3 more
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2023-01-01
Hao Lu , Yunpeng Zhang , Qing Lian +2 more
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