SfM
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Related Articles from SNS
DeblurSplat: SfM-free 3D Gaussian Splatting with Event Camera for Robust Deblurring
arXiv:2509.18898v2 Announce Type: replace Abstract: In this paper, we propose the first Structure-from-Motion (SfM)-free deblurring 3D Gaussian Splatting method via event camera, dubbed DeblurSplat. We address the motion-deblurring problem in two ways. First, we leverage the pretrained capability of the dense stereo module (DUSt3R) to directly obtain accurate initial point clouds from blurred images.
Minimal Solvers for Full-DoF Motion Estimation from Asynchronous Differential SfM
Announce Type: new Abstract: As a bio-inspired intelligent sensor, event cameras have introduced a new paradigm in the intelligent perception of spatiotemporal information and visual motion estimation, characterized by their high temporal resolution, low latency, and minimal power consumption. However, their asynchronous data streams present significant challenges to traditional synchronous, frame-based algorithms. To address these challenges, this paper presents a novel framework for full...
Edge Prediction for Roof Wireframe Reconstruction with Transformers
arXiv:2606.02406v1 Announce Type: new Abstract: This paper presents a competitive solution to the S23DR Challenge 2026, which aims to reconstruct 3D house roof wireframe models from sparse SfM point clouds and ground-level semantic segmentations and depth maps. Our proposed method utilizes an end-to-end Transformer encoder-decoder architecture inspired by DETR. To effectively process the geometric and semantic data, the sparse SfM point cloud input is dynamically subsampled based on semantic...
AGILE: Hand-Object Interaction Reconstruction from Video via Agentic Generation
arXiv:2602.04672v4 Announce Type: replace Abstract: Reconstructing dynamic hand-object interactions from monocular videos is critical for dexterous manipulation data collection and creating realistic digital twins for robotics and VR. However, current methods face two prohibitive barriers: (1) reliance on neural rendering often yields fragmented, non-simulation-ready geometries under heavy occlusion, and (2) dependence on brittle Structure-from-Motion (SfM) initialization leads to frequent...
S23DR 2026 Winning Solution
arXiv:2606.06695v1 Announce Type: new Abstract: This text presents the winning solution to the S23DR 2026 challenge for structured 3D wireframe reconstruction from sparse SfM, fitted depth, and semantic segmentations. The method treats vertices as a conditional set and denoises 64 vertex tokens with a flow-matching DiT conditioned on Perceiver-style scene tokens. A global pass predicts the coarse structure, a hull-cropped second pass refines it, and a small multi-sample consensus step keeps...
A Drug-Target Specificity Foundation Model for Off-target Prediction, Repurposing, and Generative Design
Molecular recognition - which small molecule binds which protein, and with what selectivity - governs the efficacy, safety, and discovery of every therapeutic, yet binding specificity is still determined by experimental screening or by computational methods that first predict three-dimensional structure. Transformer softmax attention is mathematically isomorphic to the Boltzmann distribution governing molecular binding at thermal equilibrium, an identity that prescribes a single...
Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching
arXiv:2606.03577v1 Announce Type: new Abstract: Wide-baseline matching (WBM) requires integrating geometric understanding, viewpoint changes, fine-grained perception, and occlusion reasoning, making it a challenging testbed for spatial reasoning in multimodal large language models (MLLMs) deployed in physical environments. However, current MLLMs lack systematic evaluation and training frameworks for these capabilities. We introduce ReasonMatch-Bench, a benchmark stratified by viewpoint...
Beyond Instance-Level Alignment and Uniformity: Semantic Factor Learning for Collaborative Filtering
Announce Type: new Abstract: Collaborative filtering (CF) is widely used in recommender systems (RecSys) due to its simplicity and efficiency. However, existing CF methods follow an instance-level learning paradigm. During the instance learning stage, a large number of uninteracted user-item instances, of which items are potential interested by the user, are incorrectly treated as true negative samples resulting in a severe limitation to the generalization and scalability of models.