Learning Category-Specific Deformable 3D Models for Object Reconstruction

By Shubham Tulsiani, Abhishek Kar, João Carreira, Jitendra Malik. 2017

79 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

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Citations in the corpus

Learning a Multi-View Stereo Machine (2017, methodology): “Learning has been used for multi-view reconstruction in the form of shape priors for objects [2, 9, 58, 20, 27, 52], or semantic class specific surface priors for scenes [22, 17, 45].”
Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency (2017, methodology): “We observe that our model when trained using only PASCAL3D data, while being category agnostic and not using ground-truth annotations for testing, performs comparably to [33] which also uses similar training data.”
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction (2021, background): “While a large variety of technical approaches was proposed [56, 62, 13, 25, 26, 46, 8], they are predominantly trained and benchmarked either on synthetic data [6], or on real datasets of speciﬁc object categories such as birds [62] or chairs [37].”
What Do Single-View 3D Reconstruction Networks Learn? (2019, methodology): “[47] approached this task with deformable models for specific object categories.”
C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion (2019, methodology): “PASCAL3D+ [39] Similar to [16, 35], we evaluate our method on the the PASCAL3D+ dataset which consists of PASCAL VOC and ImageNet images for 12 rigid object categories with a set of sparse keypoints annotated on each image (deformations still arise due to intra-class shape variations).”
Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era (2019, methodology): “[70] use the mean shape of each category of 3D models as a class-specific template.”
Learning to Infer Implicit Surfaces without 3D Supervision (2019, background): “A number of techniques have been introduced to exploit 2D training data to overcome this limitation, and use key points [26], silhouettes [4, 1, 2, 27], and shading cues [28] for supervision.”
Shape completion enabled robotic grasping (2016, background): “Others [14][15] have developed algorithms to learn 3D occupancy grids directly from 2D images.”
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation (2019, unknown): “[43] and Kanazawa et al .”
3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare (2018, background): “A good majority of the related approaches, such as [29, 36, 49, 54, 50, 47], process only a single object at a time.”
Multimodal Virtual Point 3D Detection (2021, methodology): “In the future, we plan to apply learning-based methods [50, 70] to infer the detailed 3D shape and pose from both Lidar measurements and image features.”