FPM: Fine Pose Parts-Based Model with 3D CAD Models

By Joseph J. Lim, Aditya Khosla, Antonio Torralba. 2014

100 citations recorded by the source corpus.

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

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Citations in the corpus

Single Image 3D Interpreter Network (2016, background): “Others proposed to estimate 3D viewpoint from detected 2D keypoints or edges in the image [28,36,18].”
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views (2015, methodology): “Leveraging 3D repositories, [20, 1] use 250 and 1.”
Inferring 3D Object Pose in RGB-D Images (2015, background): “There has been a lot of research on 3D scene understanding from a single RGB image [15, 28], and 3D object analysis [22, 2, 16, 39, 29].”
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients (2016, methodology): “Several recent papers have used CAD models as additional information for indoor scene understanding, by learning models of object shape [39] or hallucinating alternative viewpoints for appearance-based matching [1, 24, 23].”
Enriching object detection with 2D-3D registration and continuous viewpoint estimation (2015, methodology): “Recently, an even higher level of geometric detail was reached in the form of aligning 3D CAD model instances to real world test images [1] [12] [17] [2] [20].”
3D Shape Segmentation with Projective Convolutional Networks (2016, background): “Compositional part-based reasoning for 3D shapes has been shown to be effective for a large number of vision, robotics and virtual reality applications, such as cross-modal analysis of 3D shapes and color images [60, 24], skeletal tracking [42], objection detection in images [11, 30, 36], 3D object ”
Play and Learn: Using Video Games to Train Computer Vision Models (2016, methodology): “There are also methods that rely on indirect representations such as the HOG templates [16] or geometrical descriptors [23, 42], that can be efficiently synthesized and used.”
Joint 3D Object and Layout Inference from a Single RGB-D Image (2015, background): “While the problem has traditionally been approached using RGB images [1, 8, 23, 36, 46] and videos [42], the availability of RGB-D sensors [30] and datasets [38] nourish the hope for more accurate models of the scene [11, 16, 18, 47].”
IM2CAD (2016, background): “Another closely related line of research is 3D object and pose recognition of chairs and other objects [3, 20, 36, 23, 18, 43, 4, 44].”
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd (2015, background): “[20] achieved fine pose estimation by representing geometric and appearance information as a collection of 3D shared parts and objectness, respectively.”
Single-view reconstruction via joint analysis of image and shape collections (2015, methodology): “A natural approach to estimating the pose of an image I ∈ I is to retrieve the most similar rendered images and use their known camera poses to estimate the pose of I [Aubry et al. 2014; Lim et al. 2014; Su et al. 2014].”
From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network (2019, background): “[54], [55], [56] extended the DPM to 3D world to reason the parts in 3D and estimate the object poses, where [54] modeled the object as a 3D cuboid with both deformable faces and deformable parts, [55] proposed a 3D DPM that generates a full 3D object model with continuous appearance…”