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Found 7 papers, 6 papers with code
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
This task is challenging because 3D scenes exhibit diverse patterns, ranging from continuous ones, such as object sizes and the relative poses between pairs of shapes, to discrete patterns, such as occurrence and co-occurrence of objects with symmetrical relationships.
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like 2D object recognition, video classification etc.
H3DNet: 3D Object Detection Using Hybrid Geometric Primitives
We show how to convert the predicted geometric primitives into object proposals by defining a distance function between an object and the geometric primitives.
Ranked #7 on
3D Object Detection
on ScanNetV2
Joint Learning of Neural Networks via Iterative Reweighted Least Squares
We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks.
Path-Invariant Map Networks
Optimizing a network of maps among a collection of objects/domains (or map synchronization) is a central problem across computer vision and many other relevant fields.
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation.
