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Found 12 papers, 6 papers with code
CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers
We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design.
CAD-Recode: Reverse Engineering CAD Code from Point Clouds
Taking advantage of the exposure of pre-trained Large Language Models (LLMs) to Python code, we leverage a relatively small LLM as a decoder for CAD-Recode and combine it with a lightweight point cloud projector.
Ranked #1 on
CAD Reconstruction
on CC3D
DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference
This work presents DAVINCI, a unified architecture for single-stage Computer-Aided Design (CAD) sketch parameterization and constraint inference directly from raster sketch images.
PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervision
This work introduces PICASSO, a framework for the parameterization of 2D CAD sketches from hand-drawn and precise sketch images.
TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications.
Ranked #7 on
CAD Reconstruction
on DeepCAD
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD).
Interpretable Visual Question Answering via Reasoning Supervision
Transformer-based architectures have recently demonstrated remarkable performance in the Visual Question Answering (VQA) task.
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
An Incremental Learning framework for Large-scale CTR Prediction
In this work we introduce an incremental learning framework for Click-Through-Rate (CTR) prediction and demonstrate its effectiveness for Taboola's massive-scale recommendation service.
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery
This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors.
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
This paper addresses the problem of unsupervised discovery of object landmarks.
Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution
Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones.
