As most people navigate their everyday world, they process visual input from the environment using an eye-level perspective. Unlike robots and self-driving cars, people don’t have any “out-of-body” sensors to help guide them. Instead, a person’s sensory input is completely “egocentric”, or “from the self.” This also applies to new technologies that understand the world around us from a human-like perspective, e.g., robots navigating through unknown buildings, AR glasses that highlight objects, or assistive technology to help people run independently.

In computer vision, scene understanding is the subfield that studies how visible objects relate to the scene’s 3D structure and layout by focusing on the spatial, functional, and semantic relationships between objects and their environment. For example, autonomous drivers must understand the 3D structure of the road, sidewalks, and surrounding buildings while identifying and recognizing street signs and stop lights, a task made easier with 3D data from a special laser scanner mounted on the top of the car rather than 2D images from the driver’s perspective. Robots navigating a park must understand where the path is and what obstacles might interfere, which is simplified with a map of their surroundings and GPS positioning data. Finally, AR glasses that help users find their way need to understand where the user is and what they are looking at.

The computer vision community typically studies scene understanding tasks in contexts like self-driving, where many other sensors (GPS, wheel positioning, maps, etc.) beyond egocentric imagery are available. Yet most datasets in this space do not focus exclusively on egocentric data, so they are less applicable to human-centered navigation tasks. While there are plenty of self-driving focused scene understanding datasets, they have limited generalization to egocentric human scene understanding. A comprehensive human egocentric dataset would help build systems for related applications and serve as a challenging benchmark for the scene understanding community.

To that end, we present the Scene understanding, Accessibility, Navigation, Pathfinding, Obstacle avoidance dataset, or SANPO (also the Japanese word for ”brisk stroll”), a multi-attribute video dataset for outdoor human egocentric scene understanding. The dataset consists of real world data and synthetic data, which we call SANPO-Real and SANPO-Synthetic, respectively. It supports a wide variety of dense prediction tasks, is challenging for current models, and includes real and synthetic data with depth maps and video panoptic masks in which each pixel is assigned a semantic class label (and for some semantic classes, each pixel is also assigned a semantic instance ID that uniquely identifies that object in the scene). The real dataset covers diverse environments and has videos from two stereo cameras to support multi-view methods, including 11.4 hours captured at 15 frames per second (FPS) with dense annotations. Researchers can download and use SANPO here.

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3D scene of a real session built using the provided annotations (segmentation, depth and camera positions). The top center video shows the depth map, and the top right shows the RGB or semantic annotations.

SANPO-Real

SANPO-Real is a multiview video dataset containing 701 sessions recorded with two stereo cameras: a head-mounted ZED Mini and a chest-mounted ZED-2i. That’s four RGB streams per session at 15 FPS. 597 sessions are recorded at a resolution of 2208×1242 pixels, and the remainder are recorded at a resolution of 1920×1080 pixels. Each session is approximately 30 seconds long, and the recorded videos are rectified using Zed software and saved in a lossless format. Each session has high-level attribute annotations, camera pose trajectories, dense depth maps from CREStereo, and sparse depth maps provided by the Zed SDK. A subset of sessions have temporally consistent panoptic segmentation annotations of each instance.

The SANPO data collection system for collecting real-world data. Right: (i) a backpack with ZED 2i and ZED Mini cameras for data collection (bottom), (ii) the inside of the backpack showing the ZED box and battery pack mounted on a 3D printed container (middle), and (iii) an Android app showing the live feed from the ZED cameras (top). Left: The chest-mounted ZED-2i has a stereo baseline of 12cm with a 2.1mm focal length, and the head-mounted ZED Mini has a baseline of 6.3cm with a 2.1mm focal length.

Temporally consistent panoptic segmentation annotation protocol

SANPO includes thirty different class labels, including various surfaces (road, sidewalk, curb, etc.), fences (guard rails, walls,, gates), obstacles (poles, bike racks, trees), and creatures (pedestrians, riders, animals). Gathering high-quality annotations for these classes is an enormous challenge. To provide temporally consistent panoptic segmentation annotation we divide each video into 30-second sub-videos and annotate every fifth frame (90 frames per sub-video), using a cascaded annotation protocol. At each stage, we ask annotators to draw borders around five mutually exclusive labels at a time. We send the same image to different annotators with as many stages as it takes to collect masks until all labels are assigned, with annotations from previous subsets frozen and shown to the annotator. We use AOT, a machine learning model that reduces annotation effort by giving annotators automatic masks from which to start, taken from previous frames during the annotation process. AOT also infers segmentation annotations for intermediate frames using the manually annotated preceding and following frames. Overall, this approach reduces annotation time, improves boundary precision, and ensures temporally consistent annotations for up to 30 seconds.

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Temporally consistent panoptic segmentation annotations. The segmentation mask’s title indicates whether it was manually annotated or AOT propagated.

SANPO-Synthetic

Real-world data has imperfect ground truth labels due to hardware, algorithms, and human mistakes, whereas synthetic data has near-perfect ground truth and can be customized. We partnered with Parallel Domain, a company specializing in lifelike synthetic data generation, to create SANPO-Synthetic, a high-quality synthetic dataset to supplement SANPO-Real. Parallel Domain is skilled at creating handcrafted synthetic environments and data for machine learning applications. Thanks to their work, SANPO-Synthetic matches real-world capture conditions with camera parameters, placement, and scenery.

3D scene of a synthetic session built using the provided annotations (segmentation, depth and odometry). The top center video shows the depth map, and the top right shows the RGB or semantic annotations.

SANPO-Synthetic is a high quality video dataset, handcrafted to match real world scenarios. It contains 1961 sessions recorded using virtualized Zed cameras, evenly split between chest-mounted and head-mounted positions and calibrations. These videos are monocular, recorded from the left lens only. These sessions vary in length and FPS (5, 14.28, and 33.33) for a mix of temporal resolution / length tradeoffs, and are saved in a lossless format. All the sessions have precise camera pose trajectories, dense pixel accurate depth maps and temporally consistent panoptic segmentation masks.

SANPO-Synthetic data has pixel-perfect annotations, even for small and distant instances. This helps develop challenging datasets that mimic the complexity of real-world scenes. SANPO-Synthetic and SANPO-Real are also drop-in replacements for each other, so researchers can study domain transfer tasks or use synthetic data during training with few domain-specific assumptions.

An even sampling of real and synthetic scenes.

Statistics

Semantic classes

We designed our SANPO taxonomy: i) with human egocentric navigation in mind, ii) with the goal of being reasonably easy to annotate, and iii) to be as close as possible to the existing segmentation taxonomies. Though built with human egocentric navigation in mind, it can be easily mapped or extended to other human egocentric scene understanding applications. Both SANPO-Real and SANPO-Synthetic feature a wide variety of objects one would expect in egocentric obstacle detection data, such as roads, buildings, fences, and trees. SANPO-Synthetic includes a broad distribution of hand-modeled objects, while SANPO-Real features more “long-tailed” classes that appear infrequently in images, such as gates, bus stops, or animals.

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Distribution of images across the classes in the SANPO taxonomy.

Instance masks

SANPO-Synthetic and a portion of SANPO-Real are also annotated with panoptic instance masks, which assign each pixel to a class and instance ID. Because it is generally human-labeled, SANPO-Real has a large number of frames with generally less than 20 instances per frame. Similarly, SANPO-Synthetic’s virtual environment offers pixel-accurate segmentation of most unique objects in the scene. This means that synthetic images frequently feature many more instances within each frame.

When considering per-frame instance counts, synthetic data frequently features many more instances per frame than the labeled portions of SANPO-Real.

Comparison to other datasets

We compare SANPO to other important video datasets in this field, including SCAND, MuSoHu, Ego4D, VIPSeg, and Waymo Open. Some of these are intended for robot navigation (SCAND) or autonomous driving (Waymo) tasks. Across these datasets, only Waymo Open and SANPO have both panoptic segmentations and depth maps, and only SANPO has both real and synthetic data.

Comparison to other video datasets. For stereo vs mono video, datasets marked with ★ have stereo video for all scenes and those marked ☆ provide stereo video for a subset. For depth maps, ★ indicates dense depth while ☆ represents sparse depth, e.g., from a lower-resolution LIDAR scanner.

Conclusion and future work

We present SANPO, a large-scale and challenging video dataset for human egocentric scene understanding, which includes real and synthetic samples with dense prediction annotations. We hope SANPO will help researchers build visual navigation systems for the visually impaired and advance visual scene understanding. Additional details are available in the preprint and on the SANPO dataset GitHub repository.

Acknowledgements

This dataset was the outcome of hard work of many individuals from various teams within Google and our external partner, Parallel Domain.

Core Team: Mikhail Sirotenko, Dave Hawkey, Sagar Waghmare, Kimberly Wilber, Xuan Yang, Matthew Wilson

Parallel Domain: Stuart Park, Alan Doucet, Alex Valence-Lanoue, & Lars Pandikow.

We would also like to thank following team members: Hartwig Adam, Huisheng Wang, Lucian Ionita, Nitesh Bharadwaj, Suqi Liu, Stephanie Debats, Cattalyya Nuengsigkapian, Astuti Sharma, Alina Kuznetsova, Stefano Pellegrini, Yiwen Luo, Lily Pagan, Maxine Deines, Alex Siegman, Maura O’Brien, Rachel Stigler, Bobby Tran, Supinder Tohra, Umesh Vashisht, Sudhindra Kopalle, Reet Bhatia.

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