ViLMa Visual Localization and Mapping

Event: CVPR 2024 · Duration: 480 min · ▶ Watch on YouTube

Abstract

This video segment introduces the ViLMa workshop at CVPR 2024, focusing on visual localization and mapping. It features two keynote talks: ‘Proactive Mapping’ by Vincent Lepetit, which discusses automated 3D reconstruction using drones and information gain prediction, and ‘Visual SLAM From Optimization to Learning’ by Lukas von Stumberg, which explores combining optimization-based SLAM with deep learning for robust visual-inertial odometry and loop closure. The segment highlights the evolution of the workshop series, the importance of deep integration of sensors, and novel approaches to IMU initialization and direct image alignment. This segment features two presentations on visual SLAM and egocentric machine perception. Lukas von Stumberg discusses advanced loss formulations and benchmark results for relocalization tracking, demonstrating improvements with deep learning and introducing MonoRec for real-time dense 3D reconstruction. Jakob Engel then introduces “Localization & Mapping for Contextual AI,” highlighting the critical role of physical context for AI assistants and presenting Project Aria, a multimodal egocentric sensing device. He details new Aria-based datasets (Ego-Exo 4D, Nymeria, HOT3D) and various perception services, including SceneScript for semantic mapping and HMD2 for environment-aware motion generation. This segment introduces a system designed to track human interactions with objects in 3D environments using egocentric vision. It demonstrates how the system builds a dynamic object library, localizes objects in 3D space, and maps the movement of objects over time. The speaker highlights the potential of this technology to provide contextual signals for AI agents, enabling them to understand and interact with the physical world more effectively, such as knowing where items are located. The segment also discusses the challenges and future directions for such systems, including hardware limitations, data generation, and privacy concerns. This segment explores advanced techniques for visual localization and 3D scene reconstruction, emphasizing their application in building the metaverse. It highlights the transition from traditional descriptor-based methods to more efficient, privacy-preserving, and scalable geometric-based matching. The segment also delves into the use of Neural Radiance Fields (NeRFs) as a primary scene representation, demonstrating how their internal features can be leveraged for accurate localization and how generative models can enhance scene synthesis and editing. The discussion covers practical challenges like data capture, handling dynamic scenes, and the need for robust algorithms that generalize across diverse environments and input qualities. This segment features a presentation by Sebastian Scherer from Carnegie Mellon University on robust state estimation and mapping in challenging environments. The talk highlights the critical need for high accuracy in autonomous systems operating in diverse and difficult conditions, such as caves, tunnels, and wildfire zones. It introduces several novel methods and datasets developed to address these challenges, including AirIMU for learning uncertainty propagation in inertial odometry, AnyLoc for universal visual place recognition using self-supervised features, and geometry-informed approaches for omnidirectional stereo vision with fisheye cameras. The presentation also touches upon the use of 3D Gaussian splatting for dense RGB-D SLAM and proposes new metrics for evaluating robustness in SLAM systems. This segment features a Q&A session with Sebastian Scherer on SLAM evaluation, discussing topics like Gaussian splatting, IMU generalization, and thermal camera features. Following this, Marc Pollefeys presents on spatial intelligence for mixed reality and robotics. His talk covers 3D scene understanding, multi-device mapping and localization using HoloLens and mobile devices, and advanced Structure-from-Motion techniques. Pollefeys also introduces novel methods like PixLoc, LightGlue, GLOMAP, NICER-SLAM, GLACE, SNAP, F3Loc, OpenScene, OpenNeRF, and OpenMask3D, demonstrating their applications in industrial settings, construction sites, and urban environments. The segment concludes with a discussion on the challenges and future of spatial intelligence, emphasizing the role of foundation models and the need for robust real-world deployments.

Speakers

  • Niclas Zeller — Karlsruhe Univ. of Appl. Sciences
  • Vincent Lepetit — ENPC ParisTech, France
  • Lukas von Stumberg — Valve Corporation
  • Jakob Engel — Meta Reality Labs
  • William Smith
  • Peter Kontschieder — Director, Research Science @ Meta Reality Labs Zurich
  • Laura Leal-Taixe — NVIDIA
  • Sebastian Scherer — Carnegie Mellon University
  • Marc Pollefeys — ETH Zurich & Microsoft MR&AI Zurich Lab

Talks (11)

  • 00:00:00 — Niclas Zeller: ViLMa Visual Localization and Mapping
    • Niclas Zeller introduces the ViLMa workshop at CVPR 2024, acknowledges the organizers, provides information about the workshop, briefly reviews previous MLAD workshops, and outlines the schedule including keynote speakers and a panel discussion.
  • 00:07:20 — Vincent Lepetit: Proactive Mapping
    • The talk introduces a proactive mapping approach for 3D reconstruction using drones, focusing on automatically determining optimal camera poses to efficiently cover unknown environments by predicting information gain from potential viewpoints.
  • 00:55:32 — Lukas von Stumberg: Visual SLAM From Optimization to Learning
    • The talk presents a visual SLAM system that combines traditional optimization-based methods with deep learning techniques, focusing on robust visual-inertial odometry, loop closure, and 3D reconstruction, particularly highlighting a novel IMU initialization strategy using delayed marginalization.
  • 01:31:01 — Jakob Engel: Localization & Mapping for Contextual AI
    • Introduces the concept of ‘Age of AI’ and the importance of physical context for AI assistants, presenting Project Aria, new Aria-based datasets, and various perception services for 3D egocentric machine perception.
  • 02:39:57 — William Smith: Tracking interactions with Objects
    • A system for tracking human interactions with objects in 3D environments using egocentric vision, dynamic object mapping, and environment reconstruction for AI agents.
  • 04:07:12 — Peter Kontschieder: (More) Ingredients for Mapping the Metaverse
    • Peter Kontschieder discusses the objective of developing next-generation CV/ML algorithms for building high-fidelity 3D semantic scenes from images for the metaverse, showcasing examples of immersive 3D reconstructions and highlighting challenges with data capture, robust representations, and object recognition for scene understanding.
  • 04:53:33 — Laura Leal-Taixe: Exploring scene representations for visual localization
    • Laura Leal-Taixe introduces visual localization, highlighting practical challenges with traditional structure-based methods like storage demand, privacy risks, and maintenance complexity, and proposes a descriptor-free geometric-based matching approach and NeRF-based localization as primary scene representations.
  • 05:20:03 — Sebastian Scherer: Robust State Estimation and Mapping in Challenging Environments
    • An overview of the challenges and approaches to achieving robust state estimation and mapping for autonomous systems in difficult environments like caves, tunnels, and wildfire zones.
  • 05:25:27 — Sebastian Scherer: SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
    • An online camera tracking and reconstruction method that uses 3D Gaussian splatting for dense RGB-D SLAM, allowing for novel view synthesis and map optimization.
  • 06:39:53 — Sebastian Scherer: Example Robustness Metric Evaluation from ICCV 2023 SLAM Challenge
    • Sebastian Scherer answers audience questions about Gaussian splatting, IMU generalization, motion priors, and thermal vs. regular camera features in the context of SLAM evaluation.
  • 06:45:31 — Marc Pollefeys: Spatial Intelligence for MR and Robotics
    • Marc Pollefeys presents on spatial intelligence for mixed reality and robotics, covering 3D scene understanding, multi-device mapping, localization, and advanced SfM techniques, highlighting applications in industrial settings and future directions with foundation models.

Key Takeaways

  • The ViLMa workshop at CVPR 2024 emphasizes the integration of optimization and deep learning for robust visual localization and mapping solutions.
  • Proactive mapping strategies can significantly improve the efficiency and completeness of 3D reconstruction in unknown environments by intelligently selecting optimal camera poses.
  • Novel IMU initialization techniques, such as delayed marginalization, can enhance the robustness and accuracy of visual-inertial odometry systems, especially in challenging scenarios like constant motion.
  • Direct image alignment methods, when combined with deep learning for feature extraction, can overcome traditional limitations like sensitivity to illumination changes, leading to more robust relocalization and mapping.
  • Advanced loss formulations and deep learning techniques significantly enhance relocalization accuracy and enable real-time dense 3D reconstruction from monocular cameras.
  • Physical context, encompassing world knowledge, personal digital context, and physical environment interaction, is fundamental for developing truly useful and intelligent AI assistants.
  • Multimodal egocentric sensing devices like Project Aria facilitate the creation of rich, large-scale datasets for advancing 3D egocentric machine perception.
  • Novel methods like SceneScript and HMD2 leverage egocentric data for tasks such as semantic mapping, environment-aware motion generation, and tracking interactions with objects.
  • Tracking human interactions with objects is crucial for dynamically updating environmental reconstructions and providing rich context for AI agents.
  • Egocentric vision from lightweight wearable devices enables detailed 3D object tracking, localization, and environment reconstruction.
  • The system can build a comprehensive understanding of an environment’s state, including object locations and movements over time, which is vital for applications like finding lost items.
  • Future development focuses on improving hardware (smaller, lighter, less power-hungry sensors) and enabling real-time, on-device processing to enhance privacy and practical application.
  • Geometric-based matching offers a descriptor-free approach to visual localization, effectively addressing challenges related to storage, privacy, and descriptor maintenance.
  • Neural Radiance Fields (NeRFs) can serve as a compact and interpretable primary scene representation for visual localization, enabling the generation of synthetic data for training and leveraging internal features for accurate pose estimation.
  • Generative models are increasingly crucial for scene completion, stylization, and synthesis, pushing the boundaries of immersive metaverse experiences and enabling robust reconstruction from imperfect input data.
  • The development of algorithms that can jointly model static and dynamic scene elements, along with efficient and robust representations, is essential for building scalable and realistic metaverse environments.
  • Robustness is a paramount challenge in SLAM, especially for autonomous systems operating in visually degraded or complex environments, requiring high accuracy and reliability.
  • Novel methods like AirIMU and AnyLoc demonstrate improved performance in handling sensor degradation, appearance variations, and diverse viewpoints by incorporating uncertainty awareness and self-supervised learning.
  • Leveraging diverse datasets, including synthetic and real-world data from various conditions, is crucial for developing generalizable SLAM solutions that can perform across different scenarios.
  • Rethinking traditional SLAM metrics to include aspects like robustness and completeness, beyond just accuracy, is essential for evaluating performance in real-world applications and ensuring safe operation.
  • Spatial intelligence is crucial for enabling advanced capabilities in mixed reality and robotics, allowing devices and agents to understand and interact with their 3D environments.
  • Multi-device mapping and localization, leveraging data from various sensors and devices, can create comprehensive and continuously updated digital twins of real-world spaces.
  • Advanced SfM and localization techniques, including hybrid approaches with points, lines, and deep learning-based feature matching, are improving robustness and accuracy in challenging environments.
  • Foundation models and open-vocabulary approaches are extending 3D scene understanding to enable natural language queries for object identification, segmentation, and interaction within complex scenes.

Methods / Models / Datasets Mentioned

  • 3D Gaussian Splatting
  • ACE
  • ALTO
  • AirIMU
  • Argoverse 2 Vehicle Fleet Dataset
  • Aria Machine Perception Services
  • AutoRF
  • BPnPNet
  • BRIEF feature descriptor
  • Block-NeRF
  • CAPS
  • CLIP
  • COLMAP
  • ConsistDreamer
  • Contrastive Loss
  • CrossFire
  • D3VO (Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry)
  • DARPA SubT
  • DFNet
  • DINO
  • DINOv2
  • DM-VIO
  • DORN
  • DSAC*
  • DSM
  • DSO
  • DSO (Direct Sparse Odometry)
  • Dynamic 3D Gaussian Fields
  • Dynamic Indexing
  • EGD
  • EGN
  • Ego-Exo 4D
  • Egocentric Voxel Lifting
  • Eneg
  • Epos
  • EuRoC Datasets
  • EyeFul Tower
  • F3Loc
  • GANeRF
  • GLACE
  • GLOMAP
  • GN-Net
  • GTSAM
  • Gaussian splatting
  • GeM
  • GoMatch
  • GroundingDino
  • HAcNet
  • HLoc
  • HMD2
  • HOT3D
  • IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation
  • KITTI Datasets
  • Kitti
  • LENS
  • LM-Reloc
  • LMReloc
  • LightGlue
  • Loop-Closure
  • MACARONS: Mapping And Coverage Anticipation with RGB Online Self-supervision
  • MPD-Fusion
  • MS-Transformer
  • Mapillary
  • Metropolis
  • MixVPR
  • MonoRec
  • Monocular 3D Object Detection
  • Multi-level Neural Scene Graphs
  • MultiDiff
  • NICER-SLAM
  • NeRFLoc
  • NeRFMatch
  • NeRFMatch-Mini
  • NeRFMatch-RGB
  • NeRFMatch-Syn
  • NeRFs
  • NerfFacto
  • NetVLAD
  • NeuMap
  • NuScenes
  • Nymeria
  • OmniMVS
  • OmniVidar
  • OpenMask3D
  • OpenNeRF
  • OpenScene
  • PCA
  • PGBA (Pose Graph Bundle Adjustment)
  • PackNet
  • Panoptic Lifting
  • Panoptic Segmentation
  • PixLoc
  • Plane-Sweeping
  • PoseNet
  • Project Aria
  • PyPose
  • Quest 3
  • RTSS
  • RealEstate10K
  • SIFT
  • SLAM Mapping
  • SNAP
  • SUDS
  • ScanNet++
  • SceneFun3D
  • SceneScript
  • Segment Anything
  • Spherical Sweeping
  • SplaTAM
  • SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
  • SubT-MRS Datasets
  • SuperPoint
  • TLIO
  • TPTIO
  • TUMVI
  • Tanks and Temples
  • Tartan Air Datasets
  • TartanVO Stereo
  • VI-DSO
  • VIO
  • VLAD
  • Visual-Inertial BA

Topics

3D Reconstruction · 3D Scene Reconstruction · 3D reconstruction · 3D scene understanding · AI Assistants · AI agents · Affordances · Challenging Environments · Data Capture Challenges · Datasets · Deep Learning for SLAM · Delayed Marginalization · Dense 3D Reconstruction · Digital twins · Direct Image Alignment · Dynamic environment mapping · Egocentric Machine Perception · Egocentric vision · Fisheye Cameras · Foundation Models · Foundation models · Gaussian Splatting · Gaussian splatting · Generative Models · Geometric-based Matching · Human-object interaction · IMU Initialization · IMU generalization · Inertial Odometry · Localization · Mapping · Metaverse Mapping · Metrics for Robustness · Mixed Reality (MR) · Motion Tracking · Motion priors · Multi-device mapping · Multimodal Sensing · Neural Radiance Fields (NeRFs) · Object functionality · Object tracking · Omnidirectional Stereo Vision · Pose Estimation · Privacy in spatial computing · Proactive Mapping · Relocalization · Robotics · Robust SLAM · SLAM evaluation · Scene Representation · Semantic Mapping · Spatial intelligence · State Estimation · Structure-from-Motion (SfM) · Thermal cameras · Visual Localization · Visual Localization and Mapping · Visual Place Recognition · Visual SLAM · Wearable computing

Notes

Open for commentary — connections to other work, critiques, follow-up reading.