CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation

• URL: http://arxiv.org/abs/2311.01447v1
• Date: Thu, 2 Nov 2023 17:56:59 GMT
• Title: CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
• Authors: Jingkang Wang, Sivabalan Manivasagam, Yun Chen, Ze Yang, Ioan Andrei B\^arsan, Anqi Joyce Yang, Wei-Chiu Ma, Raquel Urtasun
• Abstract summary: Sensor simulation involves modeling traffic participants, such as vehicles, with high quality appearance and articulated geometry. Current reconstruction approaches struggle on in-the-wild sensor data, due to its sparsity and noise. We present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry.
• Score: 44.83732884335725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can be tested in simulation. Sensor simulation involves modeling traffic participants, such as vehicles, with high quality appearance and articulated geometry, and rendering them in real time. The self-driving industry has typically employed artists to build these assets. However, this is expensive, slow, and may not reflect reality. Instead, reconstructing assets automatically from sensor data collected in the wild would provide a better path to generating a diverse and large set with good real-world coverage. Nevertheless, current reconstruction approaches struggle on in-the-wild sensor data, due to its sparsity and noise. To tackle these issues, we present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry, including articulated wheels, with high-quality appearance. Our experiments show our method recovers more accurate shapes from sparse data compared to existing approaches. Importantly, it also trains and renders efficiently. We demonstrate our reconstructed vehicles in several applications, including accurate testing of autonomy perception systems.

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