68 citations · 379 across the 62 of their papers we have counts for
70 papers
WayPOP: A Panoramic Open-Set Panoptic Tracking Benchmark
Rohit Mohan, Maximilian Luz, Swarnava Chowdhury +7
Holistic perception for autonomous agents requires reliably segmenting and associating scene elements across time and camera views, including objects outside a fixed semantic taxon…
LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation
Hongli Xu, Zhaowei Lu, Junwen Huang +5
Category-level 6D pose estimation from a single RGB-D observation is inherently under-constrained, since partial visible geometry must be interpreted together with a canonical obje…
SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimation
Changxuan Li, Nadine Berner, Nassir Navab +2
Self-supervised depth estimation from monocular sequences relies on the joint learning of a depth and a pose network. Despite abundant research done to improve the depth network, e…
Language-Guided Open-World Anomaly Segmentation
Klara Reichard, Nikolas Brasch, Nassir Navab +1
Open-world and anomaly segmentation methods seek to enable autonomous driving systems to detect and segment both known and unknown objects in real-world scenes. However, existing m…
SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors
Kunyi Li, Michael Niemeyer, Sen Wang +3
Recent advances in dense 3D reconstruction have demonstrated strong capability in accurately capturing local geometry. However, extending these methods to incremental global recons…
Epipolar Geometry Improves Video Generation Models
Orest Kupyn, Théo Uscidda, Marta Tintore Gazulla +3
Video generation models have advanced significantly through the latent diffusion transformers trained with rectified flow techniques. Yet these models still struggle with geometric…
GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting
Elena Alegret, Kunyi Li, Sen Wang +5
3D scene reconstruction and understanding have gained increasing popularity, yet existing methods still struggle to capture fine-grained, language-aware 3D representations from 2D…
Self-supervised Latent Space Optimization with Nebula Variational Coding
Yida Wang, David Joseph Tan, Nassir Navab +1
Deep learning approaches process data in a layer-by-layer way with intermediate (or latent) features. We aim at designing a general solution to optimize the latent manifolds to imp…
Prior2Former -- Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation
Sebastian Schmidt, Julius Körner, Dominik Fuchsgruber +3
In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel…
From Open-Vocabulary to Vocabulary-Free Semantic Segmentation
Klara Reichard, Giulia Rizzoli, Stefano Gasperini +4
Open-vocabulary semantic segmentation enables models to identify novel object categories beyond their training data. While this flexibility represents a significant advancement, cu…
SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians
Siyun Liang, Sen Wang, Kunyi Li +5
3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering. While its vanilla representation is mainly designed for view synthesis, recen…
ESCAPE: Equivariant Shape Completion via Anchor Point Encoding
Burak Bekci, Nassir Navab, Federico Tombari +1
Shape completion, a crucial task in 3D computer vision, involves predicting and filling the missing regions of scanned or partially observed objects. Current methods expect known p…
MonoGSDF: Exploring Monocular Geometric Cues for Gaussian Splatting-Guided Implicit Surface Reconstruction
Kunyi Li, Michael Niemeyer, Zeyu Chen +2
Accurate meshing from monocular images remains a key challenge in 3D vision. While state-of-the-art 3D Gaussian Splatting (3DGS) methods excel at synthesizing photorealistic novel…
UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image
Xingyu Liu, Gu Wang, Ruida Zhang +3
Unseen object pose estimation methods often rely on CAD models or multiple reference views, making the onboarding stage costly. To simplify reference acquisition, we aim to estimat…
Neural Semantic Map-Learning for Autonomous Vehicles
Markus Herb, Nassir Navab, Federico Tombari
Autonomous vehicles demand detailed maps to maneuver reliably through traffic, which need to be kept up-to-date to ensure a safe operation. A promising way to adapt the maps to the…
RaNeuS: Ray-adaptive Neural Surface Reconstruction
Yida Wang, David Joseph Tan, Nassir Navab +1
Our objective is to leverage a differentiable radiance field \eg NeRF to reconstruct detailed 3D surfaces in addition to producing the standard novel view renderings. There have be…
Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos
Colton Stearns, Adam Harley, Mikaela Uy +4
Gaussian splatting has become a popular representation for novel-view synthesis, exhibiting clear strengths in efficiency, photometric quality, and compositional edibility. Followi…
Mixed Diffusion for 3D Indoor Scene Synthesis
Siyi Hu, Diego Martin Arroyo, Stephanie Debats +3
Generating realistic 3D scenes is an area of growing interest in computer vision and robotics. However, creating high-quality, diverse synthetic 3D content often requires expert in…
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion
Guangyao Zhai, Evin Pınar Örnek, Dave Zhenyu Chen +5
We present EchoScene, an interactive and controllable generative model that generates 3D indoor scenes on scene graphs. EchoScene leverages a dual-branch diffusion model that dynam…
3D scene generation from scene graphs and self-attention
Pietro Bonazzi, Mengqi Wang, Diego Martin Arroyo +4
Synthesizing realistic and diverse indoor 3D scene layouts in a controllable fashion opens up applications in simulated navigation and virtual reality. As concise and robust repres…
Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation
Francesco Di Felice, Alberto Remus, Stefano Gasperini +5
Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment. Yet, it presents many challenges, often related to the lack of f…
KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation
Ruida Zhang, Chenyangguang Zhang, Yan Di +4
In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometri…
Denoising Diffusion via Image-Based Rendering
Titas Anciukevičius, Fabian Manhardt, Federico Tombari +1
Generating 3D scenes is a challenging open problem, which requires synthesizing plausible content that is fully consistent in 3D space. While recent methods such as neural radiance…
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact
Mahdi Saleh, Michael Sommersperger, Nassir Navab +1
In robotics, it's crucial to understand object deformation during tactile interactions. A precise understanding of deformation can elevate robotic simulations and have broad implic…