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Found 11 papers, 7 papers with code
M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place
With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation.
Evaluating Robustness of Visual Representations for Object Assembly Task Requiring Spatio-Geometrical Reasoning
We present a novel task scenario designed to evaluate the progress in visuomotor policy learning, with a specific focus on improving the robustness of intricate assembly tasks that require both geometrical and spatial reasoning.
Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives
Recently, Implicit Neural Representations (INRs) parameterized by neural networks have emerged as a powerful and promising tool to represent different kinds of signals due to its continuous, differentiable properties, showing superiorities to classical discretized representations.
KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo
Supervised multi-view stereo (MVS) methods have achieved remarkable progress in terms of reconstruction quality, but suffer from the challenge of collecting large-scale ground-truth depth.
TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers
We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matching Transformer (FMT) to leverage intra- (self-) and inter- (cross-) attention to aggregate long-range context information within and across images.
Ranked #8 on
3D Reconstruction
on DTU
SORNet: Spatial Object-Centric Representations for Sequential Manipulation
Sequential manipulation tasks require a robot to perceive the state of an environment and plan a sequence of actions leading to a desired goal state.
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models
In this work, we introduce a general method for 3D self-supervised representation learning that 1) remains agnostic to the underlying neural network architecture, and 2) specifically leverages the geometric nature of 3D point cloud data.
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame.
DeepGMR: Learning Latent Gaussian Mixture Models for Registration
Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics.
Iterative Transformer Network for 3D Point Cloud
Recently, neural networks operating on point clouds have shown superior performance on 3D understanding tasks such as shape classification and part segmentation.
PCN: Point Completion Network
Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications.
Ranked #6 on
Point Cloud Completion
on Completion3D
