The paper proposes a novel symmetry-aware, category-level method for 9D object pose estimation that accurately estimates translation and size first, followed by rotation, achieving state-of-the-art re…

The paper introduces PIXIE, a zero-shot framework for estimating 6D pose of an object from an RGB image using only an untextured 3D model.

CIPER proposes a unified transformer framework to simultaneously perform cross-view image retrieval and precise 3-DoF pose estimation, overcoming the limitations of cascaded, separate methods.

This paper presents a lightweight network for omnidirectional human detection and relative 2D pose estimation from planar LiDAR sequences using Space-Time Blocks.

The paper introduces ProxyPose, a method for six-degree-of-freedom (6-DoF) pose tracking using a video diffusion model and a single marked pixel in the first frame.

The paper introduces BayesContact, a framework for visuo-tactile pose estimation using simulation-based inference, improving pose observability and insertion success by 30%.

PRIMA is a framework that significantly improves 3D quadruped mesh recovery by integrating biological knowledge and a test-time adaptation strategy, achieving state-of-the-art results on diverse and c…

This paper addresses robot localization in GPS-denied indoor environments using a semantic reasoning approach with a vision-language model, achieving high accuracy with a composite loss and curriculum…

This paper proposes a SemCom framework for real-time mobile 3D reconstruction, which includes a semantic transceiver and a confidence-guided geometric estimation method.

The paper proposes a pose-conditioned, permutation-equivariant denoiser to accurately reconstruct work zone geometry using noisy Ultra-Wideband (UWB) range data from connected and autonomous vehicles…

The paper presents SenseNova-Vision, a unified multimodal model for computer vision tasks using natural language instructions and optional visual prompts, trained primarily on a new corpus and requiri…

This paper introduces VIDAR, a framework for metric dense monocular reconstruction using visual-inertial odometry and Depth Anything 3.

The paper introduces MetricScenes, a new large-scale, in-the-wild dataset, and demonstrates that fine-tuning existing geometry models on this dataset significantly mitigates the scale-collapse problem…

The paper proposes MoEIoU, a novel mixture-of-experts based regression loss that adaptively models bounding-box localization errors, achieving superior convergence and accuracy in object detection.

The paper identifies a fundamental mismatch between standard pairwise ranking metrics (like AP and FPR-95) and the true assignment objective in multi-view object association, proposing a Sinkhorn-base…

CamGeo is a novel framework that improves sparse camera-conditioned image-to-video generation by distilling rich 3D geometric priors into the diffusion backbone, resulting in geometrically consistent…

The paper introduces a novel two-stage framework to achieve robust, category-agnostic object localization in-context (ICL) by optimizing attention and minimizing localization error using reinforcement…