Long-Sequence 3D Benchmark
A benchmark designed to evaluate the performance of 3D perception systems in long time-sequence scenarios. It focuses on assessing model accuracy in recognizing objects at varying distances and measuring error accumulation over extended temporal sequences.
Timeline (last 90 days)
During a demo walk of a long-sequence 3D benchmark test, the system's recognition accuracy for nearby targets was 2/2 and for distant targets was 3/10.
Long-sequence tasks tend to have larger variance because errors accumulate along the time axis, requiring a greater number of repeated experiments to reduce uncertainty.
All Facts (2)
During a demo walk of a long-sequence 3D benchmark test, the system's recognition accuracy for nearby targets was 2/2 and for distant targets was 3/10.
50%UnverifiedLong-sequence tasks tend to have larger variance because errors accumulate along the time axis, requiring a greater number of repeated experiments to reduce uncertainty.
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