All papers

The paper presents LED-BENCH, a multidisciplinary benchmark evaluating multimodal model understanding and complex reasoning across large-format engineering drawings.

Presents a model-free pipeline using smartphone-derived 3D Gaussian Splatting for novel object onboarding and 6D pose estimation.

Evaluates prompting methods and visual feature probing for temporal ego-motion reasoning in driving video-language models.

The paper proposes federated coreset subsampling strategies to enable distributed, privacy-preserving visual anomaly detection across multiple factory sites.

Optimizes multimodal prompts self-supervisedly to synthesize realistic industrial defect images for anomaly detection.

Develops a semantic 3D Gaussian Splatting pipeline with depth and region supervision for interactive metric measurements during building inspections.

The paper introduces SightCAD, an active view selection framework optimizing camera viewpoints under occlusion constraints for parametric CAD reconstruction.

The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work.

Evaluates visual representations and integration mechanisms for video-based world action models in robot manipulation tasks.

Formulates zero-shot robot manipulation from synthetic video generation as a constrained trajectory optimization problem.

Presents a dual-stream memory architecture combining recurrent compression and frame retrieval for vision-language-action robotic policies.

Evaluates whether robotic manipulation agents can learn complex multi-step assembly skills purely from sparse binary task rewards.

Synthesizes stereo manipulation data from monocular robot demonstrations using estimated depth and video diffusion models.

Proposes a camera-configurable framework for vision-language-action policies that handles variable camera counts and poses without fixed setups.

The paper proposes a compact optical-flow representation to classify tactile object rotation across Earth, Mars, Moon, and orbital gravity domains.

The paper proposes a lightweight pose-graph tracking module using relative pose constraints and geometric uncertainty for real-time 6D object tracking.

Analyzes LiDAR object detection in snow to separate atmospheric noise from performance gains caused by increased point reflections.

Learns dexterous manipulation policies from a single human video by abstracting motions into sequential scene graphs for reinforcement learning.

Presents an image dataset and an individual-tree-disjoint evaluation benchmark for automated wood species identification.

Presents an embodied coding agent framework using active physical and visual probing to refine aerial manipulation policies.

Introduces a robot world model using image-rendered actions and counterfactual post-training to improve action-following and contact prediction.

Presents a field-deployed smartphone inspection system combining deep learning and geometry to assess power distribution infrastructure conditions.

Anchors feed-forward visual geometry to LiDAR-inertial motion to produce metrically accurate trajectories and 3D Gaussian reconstructions.

Introduces AFCE, training vision-language-action policies to predict future physical effects rather than raw action chunks directly.

Proposes a simulation-grounded framework rendering real-world 3DGS scenes to supervise autonomous driving world action models.

Uses a frozen world model to continually steer generalist robot policies onto new tasks without catastrophic forgetting.

Aligns robot policy representations with retrieved pretraining examples to maintain generalization during low-data fine-tuning.

Applies convolutional neural networks and vision transformers to video feeds for automated detection of labor contractions in dairy cattle.

Proposes a lightweight fruit sorting system combining 3D convolutions and Mamba sequence modeling for rotational visual quality inspection.

Fine-tunes SAM 2.1 on multi-class building images to improve 2D segmentation for image-based 3D reconstruction.

Introduces an architecture with teacher-specific tokens and mixture-of-experts to scale multi-teacher distillation in digital pathology.

The paper presents CELL, learning pose-informative correspondence confidence end-to-end via differentiable probabilistic PnP to localize event cameras in LiDAR maps.

Deploys a quantized YOLOX-S detector on embedded hardware for onboard maritime satellite imagery selection and data reduction.

The paper presents a diffusion planner with causal factorization for precision multi-object non-prehensile placement using mobile robots.

The paper proposes a focal-stack trajectory integration framework mapping instructions to constrained geometric paths for microscopic robot manipulation.

Presents a lightweight 13.1k-parameter segmentation network for real-time crop-row detection and visual servoing on resource-constrained agricultural robots.

Proposes a multi-frame outdoor LiDAR registration network using hybrid feature and pose memory buffers to leverage temporal sequences.

Constructs a multimodal simulator dataset and trajectory prediction model for autonomous driving in urban roadway work zones.

Describes an onboard satellite pipeline combining segmentation and self-supervised feature encoding for real-time marine anomaly detection.

Builds an automated real-to-sim-to-real framework using 3D Gaussian Splatting to adapt robot manipulation policies to specific rooms.

Formulates robot action diffusion policies that condition directly on physical timestamps using time-aware rotary positional embeddings.

Uses self-supervised thermal-RGB fusion to detect drivable wheel tracks on snow-covered roads for autonomous vehicles.

Applies progressive growing generative adversarial networks to generate synthetic images for industrial defect detection on metallic parts.

The paper enhances YOLOv7 using attention mechanisms and multi-scale feature fusion for real-time metallic surface defect detection.

The paper proposes a semi-supervised industrial defect detection method using memory banks of defect-free data to derive pseudo-labels.

Proposes a bidirectional adaptive transformer framework to align RGB and 3D point cloud features for multimodal anomaly detection.

This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms.

Combines spatial and temporal transformer modules to detect smoke and fire from camera video feeds.

The paper presents a domain-generalization method to detect industrial board defects reliably across varied production environments and lighting conditions.