A point cloud, which is a set of 3D positions, is a simple, efficient, and versatile representation of 3D data. Given a point cloud and a viewpoint, which points are visible from that viewpoint? Since points themselves do not occlude one another, the real question becomes: which points would be visible if the surface they were sampled from were known? In this talk we will explore why point visibility is important, not only in computer vision but also beyond, how it can be determined, and in particular, how it can be addressed within optimization or deep learning frameworks.

Combining data from multiple sources often leads to better insights, yet many existing multi-view clustering methods are tailored to specific domains or require complex, multi-stage pipelines. We present a practical end-to-end deep learning framework that works across diverse data types, including images and tabular data. Our approach learns unified representations that capture shared structure across sources and enables consistent grouping without manual labels. The method is scalable, robust to noise, and supported by both theoretical insights and extensive experiments on ten benchmark datasets, demonstrating strong and reliable performance across varied real-world settings.

The integration of generative foundation models into geoscientific workflows represents a transformative shift in solving complex inverse problems. We explore advanced architectures for map synthesis via Conditional Flow Matching and volumetric inversion via 3D VAEs, leveraging magnetic, gravity, and drilling data. By constraining multi-modal generative priors with physical laws, we synthesize high-fidelity geologic insights from sparse, unorganized measurements. This approach accelerates mineral exploration by significantly reducing the cost and uncertainty of targeting subsurface anomalies. This synergy of cross-modal generative processes and potential field theory defines a new era of geologic intelligence.

ICE (intracardiac echocardiography) is a valuable tool in cardiac catheterization and electrophysiology (EP) procedures, assisting physicians in visualizing anatomical details and in monitoring procedures like catheter ablation, septal defect closure, left atrial appendage occlusion, and valve implantation.  Our aim is to automatically create an accurate three-dimensional surface model of Left atrium from automatic segmented boundaries of ICE images.  We propose a modified Poisson reconstruction method with additional geometric constraints, which enables the creation of accurate, highly detailed and computationally efficient surfaces from diverse sets of unorganized and sparse contours.

We present a technique and benchmark dataset for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting, where the images have limited or non-overlapping field of views. Prior work targeting extreme rotation estimation assume constrained 3D environments and emulate perspective images by cropping regions from panoramic views. However, real images captured in the wild are highly diverse, exhibiting variation in both appearance and camera intrinsics. In this work, we propose a Transformer-based method for estimating relative rotations in extreme real-world settings, and contribute the ExtremeLandmarkPairs dataset, assembled from scene-level Internet photo collections. Our evaluation demonstrates that our approach succeeds in estimating the relative rotations in a wide variety of extreme-view Internet image pairs, outperforming various baselines, including dedicated rotation estimation techniques and contemporary 3D reconstruction methods.

Robotic systems in real-world environments face conditions unseen during development, and while foundation models promise better generalization, integrating them under real-time onboard constraints remains challenging. We introduce BINA , a deployment-driven framework for online perceptual adaptation. BINA leverages online sparse supervision from a VLM to incrementally distil semantic knowledge into an onboard perception module. Beyond single-robot learning, BINA supports fleet-level knowledge aggregation, enabling scalable adaptation to new environments. Demonstrated on off-road traversability estimation, BINA rapidly converges from zero prior knowledge through operator-guided driving. Although demonstrated on traversability, BINA is task-agnostic and applicable to other perception and autonomy tasks. 

Controlling video generation is commonly achieved through training-based methods such as fine-tuning or adding control modules, which require extra optimization, data, or architectural changes. We propose a training-free approach that leverages pretrained video diffusion models to control object layout and motion using coarse spatio-temporal layouts. Our method operates in two passes: first, it steers spatial placement and temporal evolution through prompt-guided cross-attention to produce a coarse visual guide; second, this guide conditions the same model to generate a high-quality, layout-consistent video. The approach enables structured, coordinated motion with strong temporal consistency, supporting complex trajectories and multi-object interactions without additional training.

Controlling video generation is commonly achieved through training-based methods such as fine-tuning or adding control modules, which require extra optimization, data, or architectural changes. We propose a training-free approach that leverages pretrained video diffusion models to control object layout and motion using coarse spatio-temporal layouts. Our method operates in two passes: first, it steers spatial placement and temporal evolution through prompt-guided cross-attention to produce a coarse visual guide; second, this guide conditions the same model to generate a high-quality, layout-consistent video. The approach enables structured, coordinated motion with strong temporal consistency, supporting complex trajectories and multi-object interactions without additional training.

Accurate surface refinement in regions with fine geometric detail remains challenging in practical 3D acquisition pipelines, where reconstructed meshes are often limited by scan resolution and noise. Although many scanning systems capture high-resolution multi-view RGB imagery, exploiting these images for metric geometry refinement is difficult due to scale ambiguity and perspective effects inherent to wide field-of-view 2D projections.

We present a geometry-refinement pipeline that converts multi-view RGB observations into a consistent surface normal field and integrates it to deform an initial mesh toward a refined surface. The central approach is to use image-predicted surface normals as the primary refinement signal, providing scale-consistent geometric constraints that are not directly available from intensity values alone. Input views are selected and scored based on geometric visibility and viewpoint diversity to ensure robust coverage and stable convergence across the surface. To mitigate projection-induced distortions, images are undistorted and re-parameterized into locally aligned patches, with corresponding rotations applied to the predicted normals.

A U-Net model trained from scratch predicts normal maps on a dedicated network surface, while deformation is applied on a separate, explicitly upsampled sampling surface designed to absorb high-frequency detail beyond the resolution of the original reconstruction; an additional simplified surface supports efficient view selection and scoring. The refined normal field is fused by solving a Poisson formulation to recover metrically consistent vertex displacements. Experimental results demonstrate improved reconstruction fidelity in high-curvature and detail-critical regions, recovering subtle structures that are commonly smoothed or missing in scan-resolution-limited meshes.

A practical 15-minute technical session exploring the core principles of agentic workflows and how to move beyond single-agent limitations into coordinated, multi-agent AI systems. Attendees will learn key orchestration patterns, when to apply each, task decomposition strategies, overhead considerations, and an overview of leading frameworks. Participants will leave with a clear blueprint for designing robust multi-agent workflows and applying the right architecture for their AI applications.