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Correct and Certify: A New Approach to Self-Supervised 3D-Object Perception

2022-06-22 17:06:39
Rajat Talak, Lisa Peng, Luca Carlone


We consider an object pose estimation and model fitting problem, where - given a partial point cloud of an object - the goal is to estimate the object pose by fitting a CAD model to the sensor data. We solve this problem by combining (i) a semantic keypoint-based pose estimation model, (ii) a novel self-supervised training approach, and (iii) a certification procedure, that not only verifies whether the output produced by the model is correct or not, but also flags uniqueness of the produced solution. The semantic keypoint detector model is initially trained in simulation and does not perform well on real-data due to the domain gap. Our self-supervised training procedure uses a corrector and a certification module to improve the detector. The corrector module corrects the detected keypoints to compensate for the domain gap, and is implemented as a declarative layer, for which we develop a simple differentiation rule. The certification module declares whether the corrected output produced by the model is certifiable (i.e. correct) or not. At each iteration, the approach optimizes over the loss induced only by the certifiable input-output pairs. As training progresses, we see that the fraction of outputs that are certifiable increases, eventually reaching near $100\%$ in many cases. We also introduce the notion of strong certifiability wherein the model can determine if the predicted object model fit is unique or not. The detected semantic keypoints help us implement this in the forward pass. We conduct extensive experiments to evaluate the performance of the corrector, the certification, and the proposed self-supervised training using the ShapeNet and YCB datasets, and show the proposed approach achieves performance comparable to fully supervised baselines while not requiring pose or keypoint supervision on real data.

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3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot