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Found 13 papers, 4 papers with code
SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
In SemiVL, we propose to integrate rich priors from VLM pre-training into semi-supervised semantic segmentation to learn better semantic decision boundaries.
Ranked #1 on
Semi-Supervised Semantic Segmentation
on PASCAL VOC 2012 732 labeled
(using extra training data)
Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion
Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arbitrary topologies.
SoftPool++: An Encoder-Decoder Network for Point Cloud Completion
We propose a novel convolutional operator for the task of point cloud completion.
Learning Local Displacements for Point Cloud Completion
To this aim, we introduce a second model that assembles our layers within a transformer architecture.
Transformers in Action: Weakly Supervised Action Segmentation
The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense frame-wise labels.
A Divide et Impera Approach for 3D Shape Reconstruction from Multiple Views
Estimating the 3D shape of an object from a single or multiple images has gained popularity thanks to the recent breakthroughs powered by deep learning.
SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification
In this paper, we propose a method for 3D object completion and classification based on point clouds.
ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image
We propose a novel model for 3D semantic completion from a single depth image, based on a single encoder and three separate generators used to reconstruct different geometric and semantic representations of the original and completed scene, all sharing the same latent space.
Ranked #7 on
3D Semantic Scene Completion
on NYUv2
(using extra training data)
Adversarial Semantic Scene Completion from a Single Depth Image
We propose a method to reconstruct, complete and semantically label a 3D scene from a single input depth image.
Human Motion Analysis with Deep Metric Learning
Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks.
6D Object Pose Estimation with Depth Images: A Seamless Approach for Robotic Interaction and Augmented Reality
To determine the 3D orientation and 3D location of objects in the surroundings of a camera mounted on a robot or mobile device, we developed two powerful algorithms in object detection and temporal tracking that are combined seamlessly for robotic perception and interaction as well as Augmented Reality (AR).
Fits Like a Glove: Rapid and Reliable Hand Shape Personalization
We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.
A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online
This paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time.
