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Occupancy Networks: Learning 3D Reconstruction in Function Space
นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูความรู้เพิ่มเติมที่นี่
With the advent of deep neural networks, learningbased approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing highresolution geometry of arbitrary topology. Many of the stateoftheart learningbased 3D~reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this work, we propose Occupancy Networks, a new representation for learningbased 3D~reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learningbased 3D tasks.
Project: https://github.com/LMescheder/OccupancyNetworks
LaneSense® | How To | 2021 Jeep Compass
LaneSense® is one of 2021 Jeep Compass’s collision avoidance systems. The system uses proximity sensors to provide lane departure warning light. This video is useful if you’d like to explore your car warning light meanings, or learn more about your safe drive systems.
Transcript
[music playing][text on screen] This system is a driver’s convenience system, not a substitute for active driver involvement. The driver must remain aware of traffic conditions and maintain control of the vehicle.
[voice over] If equipped, LaneSense monitors your position within lane boundaries and lets you know if you start to drift.
The system relies on a forwardfacing camera to monitor lane markings and is operational at speeds above 37 mph or 60 km/h.
LaneSense defaults to off. To turn the system on, push the LaneSense button on the switch panel below the Uconnect display. “LaneSense On” will appear in the instrument cluster display.
When both lane markings are detected by the system and you drift out of the lane without activating the turn signal, LaneSense provides a visual warning and steering wheel pressure to remind you to stay in the lane.
[text on screen] Both hands must remain on the steering wheel.
[voice over] To manually override the initial warning, apply counterforce to the steering wheel at any time.
[text on screen] To manually override the initial warning, apply counterforce to the steering wheel at any time.
[voice over] If you continue to drift out of the lane, the steering wheel pressure and visual warnings will continue.
If just a single lane marking is detected and you drift over the marking without a turn signal, LaneSense provides a visual warning but applies no force to the steering wheel.
Push the LaneSense button again to turn the system off.
This video is not intended to take the place of your Owner’s Manual. For complete details and other important safety information, please see your Owner’s Information.
[Text on screen] Copyright 2020 FCA US LLC. All Rights Reserved. Jeep and Mopar are registered trademarks of FCA US LLC.
Convolutional Occupancy Networks
Recently, implicit neural representations have gained popularity for learningbased 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or largescale scenes. The key limiting factor of implicit methods is their simple fullyconnected network architecture which does not allow for integrating local information in the observations or incorporating inductive biases such as translational equivariance. In this paper, we propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. By combining convolutional encoders with implicit occupancy decoders, our model incorporates inductive biases, enabling structured reasoning in 3D space. We investigate the effectiveness of the proposed representation by reconstructing complex geometry from noisy point clouds and lowresolution voxel representations. We empirically find that our method enables the finegrained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data.
https://github.com/autonomousvision/convolutional_occupancy_networks
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