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Found 12 papers, 7 papers with code
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes Using Pre-Trained Text-to-Image Models
In particular, the third and fourth stages are iterated, with the cuts obtained in the fourth stage encouraging non-rigid alignment in the third stage to focus on regions close to the cuts.
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning.
Multi-View Representation is What You Need for Point-Cloud Pre-Training
Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction.
LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation
Bird's-Eye View (BEV) features are popular intermediate scene representations shared by the 3D backbone and the detector head in LiDAR-based object detectors.
DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
We additionally show that our approach easily extends to video super-resolution when combined with recent advances in implicit neural representation.
Deep Anomaly Detection on Tennessee Eastman Process Data
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
Comparison and Analysis of Cognitive Load under 2D/3D Visual Stimuli
However, for videos that involve calculation tasks (P > 0. 05), the difference in cognitive load induced by 2D and 3D stimuli is not as pronounced.
E-CIR: Event-Enhanced Continuous Intensity Recovery
A camera begins to sense light the moment we press the shutter button.
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
The most popular and accessible 3D representation, i. e., point clouds, involves discrete samples of the underlying continuous 3D surface.
Ranked #5 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
3D Point Cloud Classification
3D Point Cloud Linear Classification
+3
A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task.
HybridPose: 6D Object Pose Estimation under Hybrid Representations
Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e. g., because of occlusion).
Ranked #7 on
6D Pose Estimation using RGB
on Occlusion LineMOD
Image Dataset for Visual Objects Classification in 3D Printing
The rapid development in additive manufacturing (AM), also known as 3D printing, has brought about potential risk and security issues along with significant benefits.
